Authors
Affiliation

Dorofieiev,Illia

University of Lausanne

Pizzi, Alessandro

Lovato, Andrea

El Abed, Ayman

Published

November 25, 2024

Abstract

This is the abstract of the report. It should be a short summary of the project, the data, the analysis and the results. It should be concise and to the point. It should not be longer than 250 words.

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1 Introduction

Obesity has become a major global health issue, with its prevalence tripling since 1975. According to the WHO, 1.9 billion adults were overweight in 2022, with over 650 million classified as obese. In Latin America and the Caribbean, the problem is particularly pressing: as of 2022, 25% of adults were obese, with rates reaching 36.1% in Mexico and around 28% and 23% in Peru and Colombia, respectively. These alarming trends contribute to rising cases of obesity-related diseases, such as diabetes and cardiovascular issues. This project, using data from Mexico, Peru, and Colombia—77% of which is synthetically generated via SMOTE and 23% collected from 498 participants online—seeks to explore how lifestyle factors contribute to obesity in these regions. While the synthetic nature of the data limits real-world applicability, this scenario allows for the practical application of concepts from the “Data Science in Business Analytics” course, enabling us to identify key patterns in dietary habits and physical activity that contribute to obesity.

Our primary goal is to identify the most significant behavioral factors contributing to obesity in these countries by conducting exploratory analyses on lifestyle patterns and building a regression and a predictive model based on factors like diet, activity level, and demographics. Visualizations will also be developed to illustrate findings and relationships clearly, enhancing stakeholder understanding of the insights derived. Although synthetic data limits the findings’ applicability, this exercise provides valuable training in data analysis techniques and the potential insights obtainable from comprehensive, real-world data.

The main research questions this project addresses include identifying which lifestyle factors significantly impact obesity in these regions and exploring whether obesity can be predicted based on these factors. By focusing on key lifestyle elements—diet and physical activity—that influence obesity, the data used is tailored to the cultural contexts of Mexico, Peru, and Colombia. Through these insights, we aim to inform public health initiatives, providing actionable data for healthcare organizations and policymakers to address the growing obesity crisis effectively.

1.0.1 2. Data

We planned to acquire data from a publicly available dataset.

The dataset used for this project, titled “Estimation of Obesity Levels Based on Eating Habits and Physical Condition,” was sourced from the UCI Machine Learning Repository.

This dataset, available in CSV, was originally compiled by researchers at the Universidad de la Costa, Colombia, and includes of both synthetically generated data and user-collected data. The 23% of the data was collected through a web page using a survey accesible online for 30 days, in which 498 individuals provided information regarding their dietary habits, physical activity levels, and demographic data. The remaining 77% of the dataset was generated synthetically using the SMOTE algorithm (Synthetic Minority Over-sampling Technique) in Weka. SMOTE was applied to balance the dataset, addressing issues of class imbalance by generating synthetic examples for minority classes.

At the end, the obtained dataset contains 17 attributes and 2111 records.

There are limitations and challenges associated with using this data. First, the reliance on synthetic data means that the results may not accurately represent real-world scenarios, as it lacks the nuances and variability present in genuine human behaviors. Second, while the user-collected data can provide valuable insights, it may be subject to biases, such as self-reporting inaccuracies and sampling biases, which can impact the reliability of our findings. Additionally, gathering data from diverse geographical regions might pose challenges in reaching a representative sample, and we must ensure that the survey is accessible and engaging to participants to encourage participation.

Below, the steps related to the conducted analyses will be outlined.

Import dataset.

Code
library(here)
dataset <- read.csv(here("data/raw/dataset.csv"))
head(dataset)
  Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP
1 Female  21   1.62   64.0                            yes   no    2   3
2 Female  21   1.52   56.0                            yes   no    3   3
3   Male  23   1.80   77.0                            yes   no    2   3
4   Male  27   1.80   87.0                             no   no    3   3
5   Male  22   1.78   89.8                             no   no    2   1
6   Male  29   1.62   53.0                             no  yes    2   3
       CAEC SMOKE CH2O SCC FAF TUE       CALC                MTRANS
1 Sometimes    no    2  no   0   1         no Public_Transportation
2 Sometimes   yes    3 yes   3   0  Sometimes Public_Transportation
3 Sometimes    no    2  no   2   1 Frequently Public_Transportation
4 Sometimes    no    2  no   2   0 Frequently               Walking
5 Sometimes    no    2  no   0   0  Sometimes Public_Transportation
6 Sometimes    no    2  no   0   0  Sometimes            Automobile
           NObeyesdad
1       Normal_Weight
2       Normal_Weight
3       Normal_Weight
4  Overweight_Level_I
5 Overweight_Level_II
6       Normal_Weight

Upload packages.

Code
library(dplyr)
library(tidyr)
library(ggplot2)
library(corrplot)
library(ggridges)
library(cluster)
library(reshape2)

1.0.1.1 2.1 Preprocessing analyses and checks

Rename variables for clarity.

Code
  dataset <- dataset %>%
  rename(family_hist = family_history_with_overweight,
         obesity_lev = NObeyesdad,
         caloric_food = FAVC,
         vegetable_food = FCVC,
         nb_meal_day = NCP,
         food_btw_meals = CAEC,
         ch2o = CH2O,
         smoke = SMOKE,
         calorie_check = SCC,
         physical_act = FAF,
         freq_alcohol = CALC,
         use_tech = TUE,
         m_trans = MTRANS,
         gender = Gender,
         age = Age,
         weight = Weight,
         height = Height)

Control for missing values.

Code
missing_values <- colSums(is.na(dataset))
missing_values
        gender            age         height         weight    family_hist 
             0              0              0              0              0 
  caloric_food vegetable_food    nb_meal_day food_btw_meals          smoke 
             0              0              0              0              0 
          ch2o  calorie_check   physical_act       use_tech   freq_alcohol 
             0              0              0              0              0 
       m_trans    obesity_lev 
             0              0 

All columns contain complete data, with no missing values. If missing data were present, we could address it by either removing rows with missing values using > dataset_cleaned <- na.omit(dataset) or imputing missing values, for instance, by replacing missing “Age” values with the mean age (dataset$Age[is.na(dataset$Age)] <- mean(dataset$Age, na.rm = TRUE)).

Converting categorical variables to factors now enables R to correctly interpret them as discrete categories, which is essential for accurate analysis and modeling.

Code
str(dataset)
'data.frame':   2111 obs. of  17 variables:
 $ gender        : chr  "Female" "Female" "Male" "Male" ...
 $ age           : num  21 21 23 27 22 29 23 22 24 22 ...
 $ height        : num  1.62 1.52 1.8 1.8 1.78 1.62 1.5 1.64 1.78 1.72 ...
 $ weight        : num  64 56 77 87 89.8 53 55 53 64 68 ...
 $ family_hist   : chr  "yes" "yes" "yes" "no" ...
 $ caloric_food  : chr  "no" "no" "no" "no" ...
 $ vegetable_food: num  2 3 2 3 2 2 3 2 3 2 ...
 $ nb_meal_day   : num  3 3 3 3 1 3 3 3 3 3 ...
 $ food_btw_meals: chr  "Sometimes" "Sometimes" "Sometimes" "Sometimes" ...
 $ smoke         : chr  "no" "yes" "no" "no" ...
 $ ch2o          : num  2 3 2 2 2 2 2 2 2 2 ...
 $ calorie_check : chr  "no" "yes" "no" "no" ...
 $ physical_act  : num  0 3 2 2 0 0 1 3 1 1 ...
 $ use_tech      : num  1 0 1 0 0 0 0 0 1 1 ...
 $ freq_alcohol  : chr  "no" "Sometimes" "Frequently" "Frequently" ...
 $ m_trans       : chr  "Public_Transportation" "Public_Transportation" "Public_Transportation" "Walking" ...
 $ obesity_lev   : chr  "Normal_Weight" "Normal_Weight" "Normal_Weight" "Overweight_Level_I" ...
Code
#Convert Gender to factor with levels 0 for Female and 1 for Male
dataset$gender <- as.factor(dataset$gender)

#Convert family_hist, caloric_food, smoke, calorie_check to factor with levels 0 for No and 1 for Yes
dataset$family_hist <- as.factor(dataset$family_hist)
dataset$caloric_food <- as.factor(dataset$caloric_food)
dataset$smoke <- as.factor(dataset$smoke)
dataset$calorie_check <- as.factor(dataset$calorie_check)

#Convert other categorical variables to factors as before
dataset$m_trans <- as.factor(dataset$m_trans)

#Convert "obesity_lev", "food_btw_meals" and "freq_alcohol" to an ordinal factor with the correct levels
dataset$obesity_lev <- factor(dataset$obesity_lev, levels = c("Insufficient_Weight", "Normal_Weight", "Overweight_Level_I", "Overweight_Level_II", "Obesity_Type_I", "Obesity_Type_II", "Obesity_Type_III"), ordered = TRUE)

dataset$food_btw_meals <- factor(dataset$food_btw_meals, levels = c("No", "Sometimes", "Frequently", "Always"),  ordered = TRUE)

#We standardize "no" to "No" to avoid NA instead of no
dataset$freq_alcohol[dataset$freq_alcohol == "no"] <- "No"

#Now we convert it to an ordinal factor with the correct levels
dataset$freq_alcohol <- factor(dataset$freq_alcohol, levels = c("No", "Sometimes", "Frequently", "Always"), ordered = TRUE)

Using str() before and after confirms that each variable has the correct data type, preventing errors during analysis.

Code
str(dataset)
'data.frame':   2111 obs. of  17 variables:
 $ gender        : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 2 1 2 2 2 ...
 $ age           : num  21 21 23 27 22 29 23 22 24 22 ...
 $ height        : num  1.62 1.52 1.8 1.8 1.78 1.62 1.5 1.64 1.78 1.72 ...
 $ weight        : num  64 56 77 87 89.8 53 55 53 64 68 ...
 $ family_hist   : Factor w/ 2 levels "no","yes": 2 2 2 1 1 1 2 1 2 2 ...
 $ caloric_food  : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 2 1 2 2 ...
 $ vegetable_food: num  2 3 2 3 2 2 3 2 3 2 ...
 $ nb_meal_day   : num  3 3 3 3 1 3 3 3 3 3 ...
 $ food_btw_meals: Ord.factor w/ 4 levels "No"<"Sometimes"<..: 2 2 2 2 2 2 2 2 2 2 ...
 $ smoke         : Factor w/ 2 levels "no","yes": 1 2 1 1 1 1 1 1 1 1 ...
 $ ch2o          : num  2 3 2 2 2 2 2 2 2 2 ...
 $ calorie_check : Factor w/ 2 levels "no","yes": 1 2 1 1 1 1 1 1 1 1 ...
 $ physical_act  : num  0 3 2 2 0 0 1 3 1 1 ...
 $ use_tech      : num  1 0 1 0 0 0 0 0 1 1 ...
 $ freq_alcohol  : Ord.factor w/ 4 levels "No"<"Sometimes"<..: 1 2 3 3 2 2 2 2 3 1 ...
 $ m_trans       : Factor w/ 5 levels "Automobile","Bike",..: 4 4 4 5 4 1 3 4 4 4 ...
 $ obesity_lev   : Ord.factor w/ 7 levels "Insufficient_Weight"<..: 2 2 2 3 4 2 2 2 2 2 ...

Check for duplicated rows in the dataset.

Code
duplicated_rows <- sum(duplicated(dataset))
duplicated_rows
[1] 24

Keep only one instance of each duplicated row.

Code
dataset_cleaned <- dataset %>% distinct() 

Check the number of rows after removing duplicates.

Code
nrow(dataset_cleaned)
[1] 2087

Verify if there are still any duplicates.

Code
any(duplicated(dataset_cleaned))
[1] FALSE

Replace the original dataset with the cleaned one.

Code
dataset <- dataset_cleaned

Remove the cleaned dataset variable.

Code
rm(dataset_cleaned)

1.0.1.2 2.2 In-depth analysis of SMOTE’s impact and visualization of class Distribution

Code
ggplot(dataset, aes(x = obesity_lev)) +
  geom_bar(fill = "blue", color = "black") +
  theme_minimal() +
  ggtitle("Class Distribution of Obesity Levels")

Code
table(dataset$obesity_lev)

Insufficient_Weight       Normal_Weight  Overweight_Level_I Overweight_Level_II 
                267                 282                 276                 290 
     Obesity_Type_I     Obesity_Type_II    Obesity_Type_III 
                351                 297                 324 

After applying SMOTE, the distribution is noticeably more balanced across all categories, with each class showing a similar count. This outcome reflects SMOTE’s intended effect of addressing class imbalance.

1.0.1.3 2.3 Distribution analysis

Density plot for age.

Code
ggplot(dataset, aes(x = age, fill = obesity_lev)) +
  geom_density(alpha = 0.5) +
  theme_minimal() +
  ggtitle("Age Distribution by Obesity Levels")

Some peaks appear sharp and could indicate overfitting or unnatural clustering due to synthetic data (for example, a prominent peak in “Obesity Type I” around the age of 30). The categories appear to be well-separated, which may help the model learn patterns effectively, but it’s essential to ensure these separations are logical and not artifacts introduced by SMOTE.

Calculate summary statistics by obesity level.

Code
dataset %>%
  group_by(obesity_lev) %>%
  summarize(
    Age_Mean = mean(age, na.rm = TRUE),
    Age_SD = sd(age, na.rm = TRUE),
    Height_Mean = mean(height, na.rm = TRUE),
    Height_SD = sd(height, na.rm = TRUE),
    Weight_Mean = mean(weight, na.rm = TRUE),
    Weight_SD = sd(weight, na.rm = TRUE)
  )
# A tibble: 7 × 7
  obesity_lev        Age_Mean Age_SD Height_Mean Height_SD Weight_Mean Weight_SD
  <ord>                 <dbl>  <dbl>       <dbl>     <dbl>       <dbl>     <dbl>
1 Insufficient_Weig…     19.8   2.69        1.69    0.0984        50.0      5.99
2 Normal_Weight          21.8   5.12        1.68    0.0950        62.2      9.32
3 Overweight_Level_I     23.5   6.25        1.69    0.0972        74.5      8.63
4 Overweight_Level_…     27.0   8.06        1.70    0.0894        82.1      8.45
5 Obesity_Type_I         25.9   7.76        1.69    0.0984        92.9     11.5 
6 Obesity_Type_II        28.2   4.87        1.77    0.0727       115.       8.02
7 Obesity_Type_III       23.5   2.76        1.69    0.0653       121.      15.5 

The summary statistics show relatively consistent means and standard deviations for Age, Height, and Weight across obesity levels, which suggests that SMOTE has preserved the overall distribution without introducing extreme values. Interpretation: Since the means and standard deviations are similar across classes, it appears SMOTE didn’t drastically alter the dataset’s variability. This consistency supports the idea that SMOTE effectively balanced the classes without distorting key variable distributions.

Perform K-means clustering and calculate silhouette score.

Code
library(cluster)
set.seed(123)
kmeans_res <- kmeans(select(dataset, where(is.numeric)), centers = length(unique(dataset$obesity_lev)))
silhouette_score <- silhouette(kmeans_res$cluster, dist(select(dataset, where(is.numeric))))
mean_silhouette_score <- mean(silhouette_score[, "sil_width"])
mean_silhouette_score
[1] 0.4513519

Silhouette Score from K-means Clustering: The mean silhouette score of approximately 0.456 suggests a moderate level of cohesion within clusters and some separation between them. This score indicates that the clusters (representing obesity levels) are neither too distinct nor too blended. Interpretation: A score close to 0.5 generally reflects reasonable class separability without excessive artificial separability. This score suggests that SMOTE has helped create distinguishable but not overly isolated clusters, which is desirable for class balance. We conclude that SMOTE has balanced the dataset without drastically distorting it.

Creating a Numerical Dataset “dataset_num”.

Code
#Change of obesity level variable character to numeric

dataset_num <- dataset%>%
  mutate(obesity_lev = recode(obesity_lev, "Insufficient_Weight"=1, "Normal_Weight" = 2, "Overweight_Level_I" = 3, "Overweight_Level_II" = 4, "Obesity_Type_I" = 5, "Obesity_Type_II" = 6, "Obesity_Type_III" = 7,))
str(dataset_num$obesity_lev)
 num [1:2087] 2 2 2 3 4 2 2 2 2 2 ...
Code
dataset_num <- dataset_num%>%
  mutate(freq_alcohol = recode(freq_alcohol,"no"=1, "Sometimes"=2, "Frequently" =3, "Always"  =4 ))

dataset_num <- dataset_num%>%
  mutate(m_trans = recode(m_trans, "Automobile"=1, "Bike"=2, "Motorbike"=3, "Public_Transportation"=4, "Walking"=5, ))

dataset_num <- dataset_num%>%
  mutate(food_btw_meals = recode(food_btw_meals, "no"=0, "Sometimes"=1 , "Frequently"=2, "Always"=3))

dataset_num <- dataset_num%>%
  mutate(calorie_check = recode(calorie_check, "no"=0, "yes"=1 , ))

dataset_num <- dataset_num %>%
  mutate(across(where(is.factor), ~ as.numeric(.)))

str(dataset_num)
'data.frame':   2087 obs. of  17 variables:
 $ gender        : num  1 1 2 2 2 2 1 2 2 2 ...
 $ age           : num  21 21 23 27 22 29 23 22 24 22 ...
 $ height        : num  1.62 1.52 1.8 1.8 1.78 1.62 1.5 1.64 1.78 1.72 ...
 $ weight        : num  64 56 77 87 89.8 53 55 53 64 68 ...
 $ family_hist   : num  2 2 2 1 1 1 2 1 2 2 ...
 $ caloric_food  : num  1 1 1 1 1 2 2 1 2 2 ...
 $ vegetable_food: num  2 3 2 3 2 2 3 2 3 2 ...
 $ nb_meal_day   : num  3 3 3 3 1 3 3 3 3 3 ...
 $ food_btw_meals: num  1 1 1 1 1 1 1 1 1 1 ...
 $ smoke         : num  1 2 1 1 1 1 1 1 1 1 ...
 $ ch2o          : num  2 3 2 2 2 2 2 2 2 2 ...
 $ calorie_check : num  0 1 0 0 0 0 0 0 0 0 ...
 $ physical_act  : num  0 3 2 2 0 0 1 3 1 1 ...
 $ use_tech      : num  1 0 1 0 0 0 0 0 1 1 ...
 $ freq_alcohol  : num  NA 2 3 3 2 2 2 2 3 NA ...
 $ m_trans       : num  4 4 4 5 4 1 3 4 4 4 ...
 $ obesity_lev   : num  2 2 2 3 4 2 2 2 2 2 ...
Code
dataset_num
     gender      age   height    weight family_hist caloric_food vegetable_food
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2         1 21.00000 1.520000  56.00000           2            1       3.000000
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4         2 27.00000 1.800000  87.00000           1            1       3.000000
5         2 22.00000 1.780000  89.80000           1            1       2.000000
6         2 29.00000 1.620000  53.00000           1            2       2.000000
7         1 23.00000 1.500000  55.00000           2            2       3.000000
8         2 22.00000 1.640000  53.00000           1            1       2.000000
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742       1 18.83631 1.751631  80.00000           2            2       2.000000
743       2 23.57265 1.698346  75.00000           2            2       2.000000
744       1 33.70075 1.642971  74.80316           2            1       3.000000
745       1 21.84502 1.613668  68.12695           2            2       1.758394
746       2 21.00000 1.605404  68.22651           1            2       2.000000
747       1 35.12540 1.529834  62.90394           1            2       2.000000
748       1 36.76965 1.550000  62.33772           2            2       2.392665
749       1 21.86893 1.731261  78.17571           2            2       2.577427
750       2 22.54921 1.629194  70.00000           1            2       2.000000
751       1 30.95896 1.633491  68.80369           2            2       2.052152
752       2 21.01738 1.752391  79.10964           2            2       3.000000
753       2 19.62357 1.818226  85.83303           2            2       2.954996
754       2 19.63795 1.809101  85.00000           2            2       3.000000
755       2 21.41364 1.709484  75.00000           2            2       2.000000
756       2 21.02963 1.607082  67.72222           2            2       2.000000
757       1 22.66760 1.718939  75.95147           2            2       2.000000
758       2 20.00000 1.831357  89.65256           2            2       2.555401
759       1 21.81119 1.600000  66.40141           2            2       2.108711
760       1 21.00000 1.754813  77.92920           2            2       2.915279
761       2 18.12821 1.778447  80.27381           2            2       1.570089
762       2 21.00000 1.709561  75.00000           2            2       2.000000
763       1 29.08107 1.674568  71.60262           2            2       2.000000
764       2 26.35892 1.755317  82.22879           2            2       1.943130
765       2 22.71725 1.708071  75.00000           2            2       2.714447
766       2 19.81695 1.806947  85.07959           2            2       2.000000
767       1 24.02397 1.599697  64.80802           1            2       2.903545
768       1 32.59313 1.721903  72.74890           2            2       2.000000
769       2 23.00000 1.712556  75.48076           2            2       3.000000
770       1 16.94149 1.551288  54.93242           1            2       1.753750
771       1 16.17299 1.603842  65.00000           2            2       2.543563
772       1 23.71259 1.588597  62.33900           1            2       2.397280
773       1 35.19409 1.673482  73.19359           2            1       3.000000
774       2 23.17298 1.858624  88.67550           2            2       2.000000
775       1 37.21816 1.593894  63.32063           2            2       2.374640
776       2 19.07644 1.620000  69.52962           1            2       2.278644
777       1 16.30687 1.616366  67.18313           2            2       1.620845
778       1 18.29723 1.637396  70.00000           2            2       2.000000
779       1 18.61076 1.550723  60.62832           1            2       2.823179
780       1 17.45108 1.600000  65.00000           2            2       3.000000
781       2 29.74050 1.820471  87.66883           1            2       3.000000
782       1 31.53939 1.657496  73.64163           2            2       2.000000
783       2 18.02646 1.752123  80.00000           2            2       2.000000
784       2 19.47554 1.857231  88.13878           2            2       2.061952
785       1 19.00000 1.760000  79.54500           2            2       2.000000
786       1 18.00000 1.644682  68.39213           2            2       2.000000
787       1 18.27043 1.737165  76.69977           2            2       2.838969
788       1 19.81623 1.507853  64.25938           1            2       2.568063
789       2 28.39124 1.810060  87.28678           1            2       3.000000
790       2 26.75852 1.801790  86.98120           1            2       2.652958
791       2 21.03379 1.625891  70.00000           1            2       2.000000
792       2 22.42981 1.640370  70.00000           1            2       2.000000
793       2 26.65042 1.791047  83.26312           1            2       1.277850
794       2 21.61825 1.679306  75.00000           2            2       2.000000
795       1 21.01254 1.615145  68.65335           2            2       1.729824
796       1 21.07119 1.616467  68.77185           2            2       1.452524
797       2 19.74120 1.816783  86.52280           2            2       2.000000
798       2 19.21638 1.812472  86.74829           2            2       2.000000
799       1 42.24475 1.768231  75.62931           2            2       3.000000
800       2 22.28308 1.870931  89.25164           2            2       2.000000
801       2 21.08651 1.863685  89.55885           2            2       2.000000
802       2 23.45160 1.670227  75.00000           2            2       2.000000
803       2 22.74028 1.717288  75.94816           2            2       2.000000
804       2 18.90025 1.750359  79.82873           2            2       2.000000
805       2 23.17031 1.707557  75.30670           2            2       2.303367
806       1 32.99712 1.672446  74.81271           2            2       2.948425
807       1 32.50114 1.675979  74.95975           2            2       2.291846
808       1 21.93883 1.611239  67.93965           2            2       1.906194
809       1 21.90953 1.611356  68.06609           2            2       1.834155
810       1 37.45575 1.508908  63.18385           2            2       2.048582
811       1 40.00000 1.561109  62.87179           2            2       2.948248
812       1 38.94328 1.554728  63.01165           2            2       2.869436
813       1 21.98734 1.730182  78.55444           2            2       2.293705
814       1 21.19822 1.743841  78.88036           2            2       2.510583
815       1 29.32038 1.642506  69.90671           2            2       2.366949
816       1 31.25559 1.693080  71.92738           2            2       2.615788
817       2 22.85177 1.752115  79.41460           2            2       3.000000
818       2 26.00000 1.745033  80.00000           2            2       2.217267
819       2 19.78929 1.820643  85.00000           2            2       3.000000
820       2 19.89588 1.807330  85.07380           2            2       2.801514
821       2 19.69380 1.800000  85.00000           2            2       2.188722
822       2 19.22631 1.814255  85.25563           2            2       2.971351
823       2 21.65223 1.700181  75.05718           2            2       2.086093
824       1 21.68741 1.604697  68.01647           2            2       1.901611
825       1 21.45546 1.611000  68.10787           2            2       1.977298
826       1 21.00000 1.754497  77.95692           2            2       2.446872
827       1 21.00000 1.752944  77.96553           2            2       2.839048
828       2 19.33740 1.859927  87.10583           2            2       2.212320
829       2 21.70164 1.896073  90.52446           2            2       2.427689
830       2 21.62025 1.605314  66.62165           2            2       2.357496
831       1 21.01257 1.758628  78.37004           2            2       3.000000
832       1 21.01662 1.755427  78.30008           2            2       3.000000
833       1 19.00000 1.779882  80.09189           2            2       1.078529
834       2 21.00000 1.712061  75.00000           2            2       2.000000
835       2 21.00000 1.676014  75.00000           2            2       2.000000
836       1 27.89978 1.700000  74.24400           2            2       2.000000
837       2 28.82522 1.765874  82.04505           2            2       1.064162
838       2 26.04708 1.745950  80.01857           2            2       1.993101
839       2 23.00000 1.690196  75.00000           2            2       2.620963
840       2 20.00000 1.817480  85.00000           2            2       2.951180
841       1 21.83300 1.580964  65.36394           1            2       2.021446
842       1 30.25574 1.652084  73.57598           2            2       2.000000
843       1 30.61359 1.645511  69.16898           2            2       2.000466
844       2 23.24541 1.707968  75.38372           2            2       2.562100
845       1 18.00000 1.456346  55.52348           1            2       2.000000
846       1 18.00000 1.498561  55.37651           1            2       2.000000
847       1 16.95050 1.603501  65.00000           2            2       2.960080
848       1 21.83706 1.558045  63.59763           1            2       2.539150
849       1 27.00000 1.550000  62.87735           1            2       2.244142
850       1 35.48360 1.647514  73.91692           2            1       3.000000
851       2 23.80144 1.855779  86.09852           2            2       2.000000
852       2 23.36721 1.853223  87.08327           2            2       2.000000
853       1 40.00000 1.570811  62.21157           2            2       2.253371
854       1 16.38009 1.617124  67.91356           2            2       2.851664
855       1 16.86598 1.644053  67.43959           2            2       1.314150
856       1 16.09323 1.608914  65.00000           2            2       1.321028
857       1 18.00000 1.647971  68.81889           2            2       2.000000
858       1 18.83919 1.624831  69.97561           2            2       2.253998
859       1 19.72652 1.508267  61.10403           1            2       2.778079
860       1 18.94710 1.518917  60.26743           1            2       2.838037
861       1 17.78118 1.600000  65.00000           2            2       3.000000
862       2 33.10058 1.838791  87.85785           1            2       2.814453
863       1 33.25102 1.730379  75.92052           2            2       2.013782
864       1 18.98512 1.759117  80.00000           2            2       2.000000
865       1 18.87192 1.755254  80.00000           2            2       2.000000
866       2 19.47853 1.804099  85.19628           2            2       2.459976
867       2 19.42972 1.813567  86.14490           2            2       2.643183
868       1 20.97925 1.756550  78.72170           2            2       2.000000
869       1 18.86915 1.756774  79.98979           2            2       2.000000
870       1 18.23609 1.620000  69.38990           2            2       2.223990
871       1 20.90139 1.709585  75.00000           2            2       2.104105
872       1 17.08525 1.535618  57.25912           1            2       1.972545
873       2 25.78592 1.818848  87.03240           1            2       2.286481
874       2 26.78784 1.817641  87.10732           1            2       2.971588
875       2 21.03751 1.636592  70.00000           1            2       2.000000
876       2 21.00829 1.621412  70.00000           1            2       2.000000
877       2 26.70371 1.802871  85.77001           2            2       2.872121
878       2 21.73150 1.696201  75.39919           2            2       2.109162
879       2 21.05289 1.694633  75.05220           2            2       2.178889
880       1 21.00000 1.618148  68.98140           2            2       1.142468
881       2 19.24106 1.856811  88.63362           2            2       2.047069
882       1 38.93945 1.738321  86.93485           1            2       2.843709
883       1 39.96547 1.739293  80.91438           1            2       2.416044
884       2 20.26193 1.807538  85.31612           2            2       2.000000
885       2 20.31094 1.849425  85.22812           2            2       2.146598
886       2 21.42054 1.712473  75.00000           2            2       2.000000
887       1 18.84552 1.753471  80.00000           2            2       2.000000
888       2 23.56214 1.717432  75.37124           2            2       2.000000
889       2 23.11833 1.677573  75.00000           2            2       2.000000
890       1 33.73271 1.679725  74.88522           2            2       3.000000
891       1 21.57129 1.600914  68.05890           2            2       1.766849
892       1 21.16557 1.617749  68.90814           2            2       1.188089
893       2 21.00000 1.610209  68.86501           1            2       1.910176
894       2 21.00000 1.612556  69.46346           1            2       2.000000
895       1 38.69226 1.548178  62.34144           2            2       2.956671
896       1 38.95287 1.568441  62.85507           2            2       2.002796
897       1 36.63146 1.532322  62.41723           2            2       2.288604
898       1 21.99612 1.730199  78.99706           2            2       2.277077
899       1 21.95131 1.730167  78.42931           2            2       2.138334
900       2 22.01823 1.627396  70.00000           1            2       2.000000
901       1 29.93446 1.638744  69.24235           2            2       2.029634
902       1 31.16657 1.680858  71.81338           2            2       2.048216
903       1 20.53461 1.758372  79.46951           2            2       2.855700
904       2 19.97166 1.822573  85.08436           2            2       2.995599
905       2 19.62700 1.815409  85.30215           2            2       2.987148
906       2 21.92939 1.698049  75.00000           2            2       2.000000
907       1 21.00944 1.606810  67.77391           2            2       2.000000
908       1 21.53823 1.610327  67.99978           2            2       1.887951
909       2 22.82843 1.710415  75.14286           2            2       2.786008
910       2 22.81466 1.716289  75.68843           2            2       2.000000
911       2 19.57668 1.834715  89.42920           2            2       2.342323
912       1 21.81465 1.611822  67.14037           2            2       1.874935
913       1 21.97977 1.600000  66.12696           2            2       2.213135
914       1 21.00000 1.753578  77.97917           2            2       2.273548
915       1 21.02850 1.742364  78.66462           2            2       2.780699
916       2 18.72104 1.775626  80.11349           2            2       1.687569
917       2 21.00000 1.714253  75.00000           2            2       2.000000
918       2 21.00000 1.693628  75.00000           2            2       2.000000
919       1 29.46317 1.659172  71.17000           2            2       2.000000
920       2 24.28486 1.776347  82.32905           2            2       1.989905
921       2 26.28842 1.767806  82.69469           2            2       1.947405
922       2 23.45532 1.702516  75.00000           2            2       2.162519
923       2 19.63720 1.814182  85.30103           2            2       2.923916
924       1 22.23984 1.599940  65.42394           1            2       2.994480
925       1 31.62638 1.666023  72.90619           2            2       2.000000
926       2 23.00000 1.701584  75.09357           2            2       3.000000
927       2 22.87466 1.713343  75.82812           2            2       2.507841
928       1 17.42027 1.489409  53.62060           1            2       1.836554
929       1 17.80783 1.518067  55.82212           1            2       1.773265
930       1 16.24058 1.616533  65.06294           2            2       2.388168
931       1 23.25246 1.589616  65.12732           1            2       2.286146
932       1 34.77290 1.675612  73.50123           2            2       3.000000
933       2 22.76623 1.874061  89.75430           2            2       2.000000
934       1 39.21451 1.580765  62.63138           2            2       2.487167
935       2 19.85897 1.620000  69.57532           1            2       2.185938
936       1 16.37001 1.613921  66.73877           2            2       2.206399
937       1 17.99272 1.618683  67.19358           2            2       1.952987
938       2 19.17993 1.637537  70.00000           2            2       2.000000
939       1 19.62155 1.566524  61.61600           1            2       2.908757
940       1 19.75580 1.542328  63.85619           1            2       2.628791
941       1 17.09902 1.600000  65.00000           2            2       3.000000
942       1 17.25813 1.600184  65.00000           2            2       2.749629
943       2 29.15391 1.773656  87.07023           1            2       1.595746
944       1 32.27887 1.646020  74.14744           2            2       2.885178
945       1 31.79394 1.650150  73.81073           2            2       2.372494
946       2 18.01433 1.751029  80.00000           2            2       2.000000
947       2 19.68500 1.838266  89.49690           2            2       2.153639
948       2 19.50639 1.824449  87.65603           2            2       2.793561
949       1 19.00000 1.760000  79.34922           2            2       2.000000
950       1 18.00000 1.622241  68.25025           2            2       2.000000
951       1 18.00315 1.729996  75.81686           2            2       2.992329
952       1 18.05239 1.725233  75.97071           2            2       2.927409
953       1 19.77432 1.541039  64.72695           1            2       2.706134
954       2 21.18035 1.773766  89.28282           2            2       2.010684
955       2 21.79372 1.754630  89.06823           2            2       2.000000
956       2 20.88016 1.674327  80.00000           2            2       2.000000
957       2 21.18354 1.720000  80.55588           2            2       2.000000
958       2 32.77449 1.913241 101.48205           2            2       2.000000
959       2 31.32761 1.737984  82.52311           2            2       2.300408
960       2 29.95620 1.703688  82.20798           2            2       2.119643
961       2 21.40342 1.716308  80.00000           2            2       2.000000
962       2 22.59144 1.650000  80.00000           2            2       2.000000
963       1 28.58394 1.578560  65.52274           2            1       2.000000
964       2 38.82519 1.780846  85.68775           2            2       2.901924
965       2 35.21717 1.823168  91.63026           2            2       2.000000
966       1 20.39267 1.525234  65.22025           2            1       2.451009
967       1 22.15485 1.481682  61.37387           2            1       2.000000
968       2 19.10880 1.768578  87.80331           2            2       2.754646
969       1 20.70768 1.569878  69.74332           2            1       2.417635
970       1 20.63469 1.568188  67.90402           2            1       2.512719
971       2 22.05215 1.792527  89.99442           2            2       1.771693
972       2 22.86978 1.795311  89.86878           2            2       1.572230
973       1 22.90999 1.700038  80.00000           2            2       2.000000
974       1 23.00000 1.668649  80.45834           2            2       2.000000
975       2 24.67981 1.700000  84.68755           2            2       2.000000
976       2 23.88421 1.713825  83.95297           2            2       2.000000
977       1 25.46141 1.700000  78.05597           2            2       2.661556
978       2 31.33380 1.835381  91.05960           2            2       2.000000
979       2 24.10871 1.700000  80.76141           2            2       2.000000
980       1 28.83056 1.700000  78.00000           2            2       3.000000
981       2 17.57009 1.700000  83.19903           2            1       2.097373
982       1 19.02757 1.659877  77.27265           2            2       2.061461
983       2 25.63245 1.836943  96.19266           2            2       1.317729
984       2 22.94313 1.819614  93.08642           2            2       1.882235
985       2 23.28555 1.717775  85.31264           2            2       2.951591
986       2 23.00000 1.791867  90.00000           2            2       2.067817
987       1 25.19291 1.700000  80.74966           2            2       2.545270
988       2 34.38991 1.681080  83.56803           2            2       2.000000
989       1 26.59589 1.660756  78.57479           2            2       2.000000
990       2 55.24625 1.769269  80.49134           1            2       2.000000
991       2 34.54356 1.765188  85.00000           2            2       2.694281
992       2 21.80816 1.650000  80.00000           2            2       2.000000
993       2 18.11828 1.654757  80.00000           2            2       2.821977
994       1 42.18902 1.647768  79.16531           2            2       2.000000
995       2 22.00000 1.691303  80.53900           2            2       2.000000
996       1 28.77085 1.532897  65.03188           2            1       2.000000
997       1 25.48338 1.565288  64.84863           2            1       2.000000
998       1 21.67315 1.500000  63.65233           2            1       2.000000
999       1 23.46954 1.507106  64.81411           2            1       2.252472
1000      2 23.00000 1.700740  81.32297           2            2       2.000000
1001      2 23.00000 1.715118  81.65078           2            2       2.000000
1002      1 38.46454 1.696423  78.96792           2            2       3.000000
1003      1 37.49618 1.653088  80.00000           2            2       2.033745
1004      1 33.69024 1.681842  77.42646           2            2       3.000000
1005      1 34.36969 1.652202  77.13322           2            2       2.595128
1006      2 31.42657 1.848683  99.00000           2            2       2.759286
1007      2 34.28825 1.835678  96.01851           2            2       2.000000
1008      1 18.86388 1.560029  71.72807           2            1       3.000000
1009      1 18.95114 1.621048  72.10586           2            1       3.000000
1010      2 19.67188 1.699474  78.00000           2            1       1.925064
1011      2 50.83256 1.745528  82.13073           2            2       2.000000
1012      2 17.97157 1.720379  85.00000           2            2       2.000000
1013      2 18.00000 1.758787  85.05056           2            2       2.846981
1014      2 18.70177 1.704908  81.38422           2            2       2.650629
1015      1 34.38968 1.691322  77.56160           2            2       3.000000
1016      2 17.17848 1.786290  95.27739           2            2       2.000000
1017      2 33.08160 1.705617  83.01697           2            2       2.000000
1018      2 33.27045 1.733439  84.75383           2            2       2.631565
1019      1 36.31029 1.701397  83.00000           2            2       2.000000
1020      1 20.00000 1.625236  74.43336           2            1       2.522399
1021      1 18.54944 1.545196  72.46786           2            1       3.000000
1022      2 34.24315 1.843172  92.69551           2            2       2.000000
1023      2 23.32012 1.705813  82.01196           2            2       2.784471
1024      2 19.70310 1.704141  78.79094           2            1       1.650505
1025      2 19.22326 1.706082  78.07353           2            1       1.961347
1026      2 29.69560 1.842943  93.79806           2            2       2.133955
1027      2 27.00000 1.880992  99.62378           2            2       2.684528
1028      2 21.02766 1.726774  82.85375           2            2       2.265973
1029      2 33.01526 1.731960  84.31561           2            2       2.000000
1030      2 18.00000 1.751278  86.96363           2            1       3.000000
1031      2 30.55310 1.780448  88.43195           2            2       2.000000
1032      2 33.00000 1.850000  97.92035           2            2       2.000000
1033      2 24.91199 1.785241  88.03748           2            2       1.306844
1034      2 24.44485 1.718845  86.31989           2            2       2.000000
1035      1 19.91125 1.530248  68.85097           2            1       2.258795
1036      1 34.20441 1.664927  80.38608           2            2       2.000000
1037      1 34.28168 1.673333  77.20569           2            2       2.689929
1038      2 23.38437 1.725587  82.48021           2            2       2.712747
1039      1 43.23840 1.733875  86.94538           2            2       2.353603
1040      1 45.00000 1.675953  79.66832           2            2       2.598051
1041      2 22.08739 1.786238  89.80249           2            2       1.718156
1042      2 21.37804 1.718534  80.00000           2            2       2.000000
1043      2 31.39828 1.919543 101.54459           2            2       2.000000
1044      2 31.48449 1.707613  82.58689           2            2       2.795086
1045      2 21.70916 1.658393  80.00000           2            2       2.000000
1046      1 30.71138 1.536819  65.91269           2            1       2.030256
1047      2 34.82144 1.805459  90.13868           2            2       2.000000
1048      1 20.84336 1.521008  67.08312           2            1       2.493448
1049      2 19.44364 1.744733  87.27989           2            2       2.442536
1050      1 20.49208 1.529223  65.14041           2            1       2.003951
1051      2 22.94435 1.799742  89.98168           2            2       1.341380
1052      1 23.00000 1.681314  80.03720           2            2       2.000000
1053      2 24.06894 1.706912  85.74373           2            2       2.000000
1054      1 23.72871 1.663509  80.00000           2            2       2.000000
1055      2 29.21602 1.752265  88.09606           2            2       2.000000
1056      2 24.75151 1.735343  83.33772           2            2       2.607335
1057      1 28.97779 1.700000  78.00000           2            2       3.000000
1058      2 17.44159 1.700000  83.41407           2            1       2.061384
1059      1 19.12615 1.633794  77.85853           2            1       2.696381
1060      2 24.12259 1.856759  95.88706           2            2       1.116068
1061      2 23.00000 1.774330  90.00000           2            2       2.116432
1062      1 28.82419 1.700000  78.00000           2            2       3.000000
1063      2 34.20461 1.719509  84.41652           2            2       2.031246
1064      1 28.16780 1.658202  78.00000           2            2       2.938031
1065      2 55.13788 1.657221  80.99321           2            2       2.000000
1066      2 34.97037 1.713348  84.72222           2            2       2.884212
1067      2 20.98683 1.677178  80.37958           2            2       2.000000
1068      1 38.37806 1.678050  77.22457           2            2       2.444599
1069      2 22.18881 1.717722  81.92991           2            2       2.000000
1070      1 21.08238 1.486484  60.11799           2            1       2.000000
1071      1 25.29320 1.503379  64.34246           2            1       2.000000
1072      2 23.00000 1.718981  81.66995           2            2       2.000000
1073      1 39.17003 1.688354  79.27890           2            2       3.000000
1074      1 34.04423 1.665807  77.09897           2            2       2.976975
1075      2 33.18213 1.838441  97.02925           2            2       2.000000
1076      1 19.82200 1.653431  75.09044           2            1       2.766036
1077      2 19.14971 1.699818  78.00000           2            1       1.096455
1078      2 46.49186 1.718097  88.60088           2            2       2.129969
1079      2 17.89478 1.731389  84.06488           2            1       2.019674
1080      2 18.88485 1.699667  79.67793           2            1       2.735297
1081      1 38.38418 1.698626  78.63731           2            2       3.000000
1082      2 22.67568 1.823765  96.94526           2            2       1.588782
1083      2 33.04912 1.708742  83.00164           2            2       2.000000
1084      1 37.20517 1.667469  80.99337           2            2       2.010540
1085      1 19.02742 1.588147  73.93927           2            1       3.000000
1086      2 37.49244 1.835024  92.36836           2            2       2.000000
1087      2 24.65760 1.708800  83.52011           2            2       2.689577
1088      2 18.01172 1.680991  79.75292           2            2       2.413156
1089      2 27.34974 1.835271  97.58826           2            2       2.923433
1090      2 18.00000 1.742819  86.56515           2            2       3.000000
1091      2 33.00928 1.741192  84.77335           2            2       2.000000
1092      2 18.00000 1.759721  86.08050           2            2       2.882522
1093      2 30.07937 1.810616  92.86025           2            2       2.000000
1094      2 25.03591 1.763101  88.53203           2            2       1.973499
1095      1 18.19832 1.543338  71.79998           2            1       3.000000
1096      1 35.45633 1.651812  79.43792           2            2       2.156065
1097      2 23.80679 1.732492  84.55780           2            2       2.992205
1098      1 39.39257 1.706349  85.72079           2            2       2.944287
1099      2 21.38426 1.780679  89.66741           2            2       1.780746
1100      2 22.73041 1.758687  89.67365           2            2       2.164062
1101      2 21.20563 1.704573  80.00000           2            2       2.000000
1102      2 21.33343 1.716545  80.00581           2            2       2.000000
1103      2 32.78710 1.903832  99.81244           2            2       2.000000
1104      2 32.61002 1.742538  83.39098           2            2       2.247704
1105      2 30.02260 1.747739  83.31416           2            2       2.011656
1106      2 21.12305 1.717037  80.00000           2            2       2.000000
1107      1 22.98985 1.650000  80.00000           2            2       2.000000
1108      1 24.32015 1.577921  65.29318           2            1       2.034140
1109      2 37.27530 1.746055  83.28223           2            2       2.746408
1110      2 34.09817 1.841151  91.79810           2            2       2.000000
1111      1 21.00128 1.517998  64.69625           2            1       2.123900
1112      1 21.95994 1.483284  62.89428           2            1       2.000000
1113      2 19.79527 1.758972  87.86143           2            2       2.332074
1114      1 19.09002 1.562434  70.44277           2            1       2.748243
1115      1 20.82045 1.547066  67.47328           2            1       2.303656
1116      2 22.08706 1.792435  89.99873           2            2       1.904732
1117      2 22.18576 1.784555  89.83669           2            2       1.979944
1118      1 22.98096 1.700216  80.47359           2            2       2.000000
1119      2 23.00000 1.672101  80.93973           2            2       2.000000
1120      2 24.19090 1.700000  84.84935           2            2       2.000000
1121      2 23.94003 1.721348  83.98671           2            2       2.407817
1122      1 26.59163 1.700000  78.00839           2            2       2.734314
1123      2 31.26463 1.803129  91.05222           2            2       2.000000
1124      1 23.62916 1.700020  80.42043           2            2       2.000000
1125      1 26.20813 1.700000  78.04134           2            2       2.784383
1126      2 17.68906 1.713599  83.28575           2            2       2.009952
1127      2 19.16097 1.662728  77.47320           2            1       2.008656
1128      2 25.45763 1.837399  95.95203           2            2       1.122127
1129      2 22.98800 1.808270  91.36329           2            2       1.963965
1130      2 23.60319 1.714508  85.13711           2            2       2.319776
1131      2 22.88256 1.793451  89.90926           2            2       1.899116
1132      1 23.58606 1.700499  81.10860           2            2       2.042762
1133      2 34.43267 1.694997  84.45142           2            2       2.129668
1134      1 25.47300 1.688911  78.43449           2            2       2.182401
1135      2 55.02249 1.673394  80.40031           2            2       2.000000
1136      2 34.64704 1.769499  85.00000           2            2       2.802696
1137      2 21.99733 1.650000  80.00000           2            2       2.000000
1138      2 18.18182 1.662669  79.86355           2            1       2.492758
1139      1 41.74333 1.678610  79.84925           2            2       2.733129
1140      2 21.47308 1.694423  80.31127           2            2       2.000000
1141      1 27.75719 1.505437  63.89207           2            1       2.000000
1142      1 25.11354 1.505387  63.71522           2            1       2.000000
1143      1 21.82165 1.491441  62.26991           2            1       2.000000
1144      1 20.67097 1.509408  64.85295           2            1       2.294067
1145      2 22.64979 1.685045  81.02212           2            2       2.000000
1146      2 23.15431 1.723072  82.33846           2            2       2.315932
1147      1 38.09739 1.696954  78.15604           2            2       3.000000
1148      1 37.64218 1.676095  79.07974           2            2       2.802128
1149      1 34.17679 1.681021  77.39218           2            2       2.796060
1150      1 34.23108 1.654067  79.69728           2            2       2.086898
1151      2 31.96540 1.840708  98.25942           2            2       2.333503
1152      2 33.18566 1.836007  97.41642           2            2       2.000000
1153      1 18.74119 1.556789  71.78818           2            1       3.000000
1154      1 19.95526 1.589100  72.71361           2            1       3.000000
1155      2 19.68489 1.700627  78.04821           2            1       1.653081
1156      2 47.70610 1.743935  84.72920           2            2       2.535315
1157      2 17.99701 1.742654  85.00000           2            1       2.000000
1158      2 17.97179 1.754441  85.00288           2            2       2.765769
1159      2 20.43272 1.719342  80.79081           2            2       2.215464
1160      1 33.95443 1.682253  77.43168           2            2       3.000000
1161      2 17.03906 1.799902  95.41967           2            2       2.000000
1162      2 33.07014 1.709428  83.01403           2            2       2.000000
1163      2 34.99383 1.752456  84.78376           2            2       2.631650
1164      2 35.71946 1.685947  83.32580           2            2       2.000000
1165      1 19.00573 1.599999  73.51387           2            1       2.942154
1166      1 18.85047 1.550053  72.95180           2            1       3.000000
1167      2 31.54075 1.828092  91.49572           2            2       2.000000
1168      2 23.33539 1.713380  82.08555           2            2       2.716909
1169      2 19.74963 1.680764  79.62146           2            1       1.837460
1170      2 19.56550 1.705584  78.02563           2            1       1.936479
1171      2 30.35775 1.841852  93.61425           2            2       2.045027
1172      2 27.00000 1.834986  99.08348           2            2       2.760607
1173      2 21.38034 1.724839  82.27635           2            2       2.094490
1174      2 33.15190 1.685127  83.98690           2            2       2.000000
1175      2 18.00000 1.750097  86.37214           2            2       2.907062
1176      2 32.24159 1.757961  87.90602           2            2       2.000000
1177      2 31.66281 1.848965  98.91226           2            2       2.399531
1178      2 24.34741 1.789193  89.39359           2            2       1.521604
1179      2 24.36212 1.716677  85.26134           2            2       2.000000
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1181      1 35.43206 1.663178  80.13517           2            2       2.000000
1182      1 33.86426 1.679299  77.35542           2            2       2.768020
1183      2 23.80718 1.729177  82.52724           2            2       2.742796
1184      2 39.58581 1.719153  86.46484           2            2       2.325623
1185      1 45.82127 1.687326  80.41400           2            2       2.076689
1186      2 25.92975 1.772449 105.00000           2            2       3.000000
1187      2 26.04274 1.837117 105.71222           2            2       3.000000
1188      2 30.55176 1.784377 102.87251           2            2       2.271306
1189      2 39.75957 1.792507 101.78010           2            2       2.333610
1190      1 31.38640 1.556579  78.23334           2            2       2.136830
1191      1 25.70629 1.585547  80.35126           2            2       2.000000
1192      1 43.60490 1.569234  81.82729           2            2       2.909853
1193      1 42.31607 1.583943  81.93640           2            2       2.490507
1194      1 22.65432 1.621233  82.00000           2            2       1.063449
1195      1 22.67994 1.608400  82.60398           2            2       2.699282
1196      2 24.07952 1.733439  97.91187           2            2       2.000000
1197      2 21.19615 1.650000  88.47236           2            2       2.427700
1198      1 22.59658 1.650052  87.27255           2            2       2.875990
1199      1 23.00000 1.644161  84.34041           2            2       2.177243
1200      1 37.35629 1.559499  77.26813           2            2       2.000000
1201      2 22.75465 1.734237  95.00000           2            2       2.000000
1202      2 23.25291 1.775815  97.81302           2            2       2.000000
1203      1 22.02544 1.640426  87.34416           2            2       3.000000
1204      1 18.08677 1.650000  82.13682           2            2       3.000000
1205      2 25.99439 1.753321 107.99881           2            2       3.000000
1206      1 40.82151 1.553026  77.74518           2            2       2.000000
1207      2 23.00000 1.706525  90.50006           2            2       2.000000
1208      1 37.95537 1.560648  80.00000           2            2       2.846452
1209      2 31.31559 1.673352  90.00000           2            2       2.190110
1210      2 22.66156 1.660324  94.18917           2            2       2.000000
1211      2 40.31779 1.786318  98.44731           2            2       2.000000
1212      2 23.36565 1.757691  95.36180           2            2       2.000000
1213      2 21.72127 1.712163  98.69997           2            2       2.000000
1214      2 35.38949 1.780000 100.84763           2            2       2.069267
1215      1 22.06146 1.600000  82.04032           2            2       1.362441
1216      1 23.00000 1.610820  82.53299           2            2       2.096630
1217      1 23.00000 1.665199  83.15115           2            2       2.928234
1218      1 40.95159 1.542122  80.00000           2            2       2.000000
1219      1 39.13563 1.507867  79.58958           2            2       2.000000
1220      2 26.27162 1.660955  90.00000           2            2       2.000000
1221      1 23.77923 1.553127  78.00000           2            2       2.000000
1222      2 20.58698 1.781952 102.91061           2            2       2.000000
1223      2 21.66948 1.750000  96.94025           2            2       2.000000
1224      1 23.00000 1.615854  80.61532           2            2       2.000000
1225      2 20.82596 1.793378 105.65504           2            2       2.000000
1226      2 18.17802 1.854780 114.77484           2            2       2.000000
1227      1 18.00000 1.685633  90.03267           2            2       2.496455
1228      1 18.26770 1.706343  93.34884           2            2       1.708250
1229      2 23.00000 1.791415 105.13807           2            2       2.262171
1230      2 23.00000 1.753716 106.49141           2            2       2.740633
1231      2 20.98590 1.780503 103.18953           2            2       2.000000
1232      2 21.50494 1.819867 106.03847           2            2       2.000000
1233      2 18.00000 1.791174 108.96060           2            2       2.000000
1234      2 18.00000 1.820930 108.74201           2            2       2.000000
1235      1 19.44266 1.627812  82.00000           2            2       1.081585
1236      1 22.86502 1.632118  82.00000           2            2       2.587789
1237      2 21.94858 1.773594 105.00079           2            2       2.252653
1238      2 21.21462 1.930416 118.56051           2            2       2.000000
1239      2 22.27786 1.947406 116.89311           2            2       2.000000
1240      1 37.83295 1.610867  80.00000           2            2       2.036613
1241      1 37.63177 1.513202  75.41065           2            2       2.000000
1242      2 17.67390 1.738465  97.18547           2            2       2.000000
1243      1 37.52455 1.567915  76.12978           2            2       2.000000
1244      1 43.51067 1.587546  76.12611           2            2       2.000000
1245      2 18.00000 1.820385 108.39500           2            2       2.000000
1246      2 18.00000 1.792239 108.24438           2            2       2.000000
1247      2 29.50629 1.826970 108.75150           2            2       2.000000
1248      1 18.00000 1.670058  86.24268           2            2       2.609123
1249      2 24.73331 1.639383  91.26709           2            2       1.036159
1250      2 29.00000 1.682916  89.99167           2            2       1.851262
1251      1 18.00000 1.609321  84.49316           2            2       2.000000
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1253      2 19.08959 1.700164  99.20470           2            2       2.000000
1254      1 18.31266 1.600740  82.24983           2            2       2.501236
1255      2 18.26008 1.801848 104.74191           2            2       2.000000
1256      2 26.00429 1.844751 105.02581           2            2       3.000000
1257      2 25.99475 1.811602 106.04214           2            2       3.000000
1258      2 36.72662 1.787521 100.62455           2            2       2.031185
1259      2 38.29726 1.789109 100.06627           2            2       2.014194
1260      1 30.16341 1.533364  78.03038           2            2       2.028225
1261      1 28.77004 1.580858  79.71349           2            2       2.000000
1262      1 42.33728 1.646390  86.63986           2            2       2.816460
1263      1 47.28337 1.643786  81.97874           2            2       2.037585
1264      1 22.51879 1.634342  82.41448           2            2       1.853314
1265      1 22.83631 1.604893  82.00000           2            2       1.008760
1266      2 23.00000 1.654961  94.79058           2            2       2.000000
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1268      2 22.72045 1.650000  89.13921           2            2       2.103335
1269      2 23.88757 1.657995  90.00000           2            2       2.000000
1270      1 23.00000 1.634688  82.62800           2            2       2.060922
1271      1 23.00000 1.628168  84.49798           2            2       2.058687
1272      1 38.14885 1.557808  79.66169           2            2       2.000000
1273      1 37.96543 1.560199  80.00000           2            2       2.533605
1274      2 22.77161 1.750384  95.32428           2            2       2.000000
1275      2 22.58237 1.753081  95.26909           2            2       2.000000
1276      1 21.00805 1.650000  88.12654           2            2       2.457547
1277      1 22.59103 1.650012  87.67615           2            2       2.983851
1278      1 16.12928 1.650000  85.58348           2            2       2.954996
1279      1 16.91384 1.634832  85.80381           2            2       2.061969
1280      2 29.43879 1.770126 108.60272           2            2       3.000000
1281      2 29.88130 1.758189 108.72189           2            2       3.000000
1282      1 43.59200 1.595165  77.35474           2            2       2.000000
1283      1 40.99318 1.567756  79.49363           2            2       2.000000
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1285      2 23.00000 1.735659  93.42920           2            2       2.000000
1286      1 37.59795 1.629010  80.00000           2            2       2.450784
1287      1 37.53207 1.615385  80.00000           2            2       2.972426
1288      2 31.63005 1.671705  90.00000           2            2       2.467002
1289      2 31.64108 1.676595  89.99381           2            2       2.934671
1290      2 21.87248 1.699998  95.56443           2            2       2.000000
1291      2 21.83489 1.722785  94.09418           2            2       2.000000
1292      2 36.02397 1.670667  90.57593           2            2       2.903545
1293      2 39.65656 1.789992  98.02177           2            2       2.043359
1294      2 24.18489 1.768834  97.44974           2            2       2.000000
1295      2 23.23730 1.761008  97.82934           2            2       2.000000
1296      2 35.32211 1.780000 102.26596           2            2       2.787589
1297      2 31.38798 1.810215 105.25435           2            2       2.397280
1298      1 23.00000 1.608469  82.95480           2            2       2.150054
1299      1 23.00000 1.630357  83.10110           2            2       2.977585
1300      1 22.67945 1.628260  82.96794           2            2       2.274491
1301      1 22.48089 1.605662  82.47038           2            2       1.557287
1302      1 41.40386 1.567973  81.05685           2            2       2.152264
1303      1 40.70277 1.548403  80.00000           2            2       2.000000
1304      2 29.38924 1.681855  90.00000           2            2       2.088410
1305      2 30.96742 1.688436  90.00000           2            2       2.180047
1306      1 23.47416 1.577115  78.00000           2            2       2.000000
1307      1 24.31761 1.586751  79.10903           2            2       2.000000
1308      2 17.05291 1.710616  99.86025           2            2       2.000000
1309      2 18.04892 1.721384  99.43061           2            2       2.000000
1310      1 22.87522 1.624367  82.00000           2            2       1.826885
1311      1 22.88472 1.622787  82.00000           2            2       1.754401
1312      2 18.72957 1.855433 112.87528           2            2       2.000000
1313      2 21.01112 1.856315 118.18380           2            2       2.000000
1314      1 18.60350 1.681719  90.67187           2            2       1.524428
1315      1 21.10606 1.722884  92.94925           2            2       1.164062
1316      2 23.00000 1.799406 106.52881           2            2       2.123159
1317      2 21.98085 1.819875 106.04852           2            2       2.000000
1318      2 21.01666 1.790172 105.04219           2            2       2.259679
1319      2 21.72738 1.783782 105.00028           2            2       2.044326
1320      2 18.00000 1.783906 109.20761           2            2       2.000000
1321      2 18.00000 1.844218 109.19553           2            2       2.000000
1322      1 19.43166 1.631662  82.00000           2            2       2.843456
1323      1 18.16632 1.649553  82.32395           2            2       2.864776
1324      2 22.35203 1.754711 105.00071           2            2       2.871137
1325      2 22.46935 1.762515 105.00010           2            2       2.282803
1326      2 19.52470 1.942725 121.65798           2            2       2.000000
1327      2 20.49148 1.975663 120.70293           2            2       2.000000
1328      1 39.21340 1.586301  80.00000           2            2       2.020502
1329      1 38.09875 1.598448  80.00000           2            2       2.020785
1330      2 19.03403 1.703098  98.29665           2            2       2.000000
1331      2 17.07365 1.706996  99.54470           2            2       2.000000
1332      1 38.54727 1.526472  75.15035           2            2       2.000000
1333      1 38.68379 1.501993  76.64294           2            2       2.000000
1334      2 18.00000 1.784402 108.41312           2            2       2.000000
1335      2 18.00000 1.792687 108.20455           2            2       2.000000
1336      2 29.62280 1.814052 109.41162           2            2       2.407817
1337      2 30.79626 1.789421 109.59945           2            2       2.214980
1338      1 18.00000 1.686904  90.00405           2            2       2.737149
1339      1 18.10709 1.703259  91.49968           2            2       1.899793
1340      2 31.33509 1.665798  89.73860           2            2       2.274164
1341      2 29.00000 1.666710  89.04815           2            2       1.897796
1342      1 18.01190 1.650000  84.21053           2            2       2.859160
1343      1 18.00000 1.649439  84.89774           2            2       2.000000
1344      2 21.07736 1.702002  97.97160           2            2       2.000000
1345      2 21.05202 1.693820  99.53097           2            2       2.000000
1346      1 18.07826 1.622999  82.40308           2            2       2.340405
1347      1 18.06358 1.633675  82.45958           2            2       2.921576
1348      2 20.41883 1.836669 105.25754           2            2       2.000000
1349      2 20.58098 1.798354 103.70542           2            2       2.000000
1350      2 26.03242 1.824432 105.13196           2            2       3.000000
1351      2 25.95536 1.830384 105.47931           2            2       3.000000
1352      2 26.01545 1.829907 105.43617           2            2       3.000000
1353      2 25.92074 1.823755 105.80016           2            2       2.927110
1354      2 29.63371 1.834842 105.19936           2            2       2.805533
1355      2 32.89564 1.783901 103.77137           2            2       2.902469
1356      2 38.74831 1.782067 103.58634           2            2       2.845961
1357      2 39.68585 1.781032  96.30385           2            2       2.252698
1358      1 33.22681 1.557943  77.64772           2            2       2.020910
1359      1 26.22007 1.560258  78.43590           2            2       2.108163
1360      1 23.09022 1.596586  80.72620           2            2       1.518966
1361      1 24.56563 1.620930  81.71823           2            2       2.260543
1362      1 42.58628 1.571417  81.91881           2            2       2.522183
1363      1 43.71939 1.584322  80.98650           2            2       2.186322
1364      1 43.37634 1.582523  81.91945           2            2       2.766320
1365      1 39.64895 1.572791  80.08652           2            2       2.071622
1366      1 22.69399 1.627908  82.00000           2            2       1.918251
1367      1 22.67624 1.620938  82.28319           2            2       1.967061
1368      1 22.80482 1.613119  82.63616           2            2       2.964419
1369      1 22.85712 1.626860  82.41019           2            2       2.640801
1370      2 23.09635 1.728183  97.95990           2            2       2.000000
1371      2 22.81542 1.732694  98.44113           2            2       2.000000
1372      1 21.02064 1.650000  88.12944           2            2       2.870152
1373      1 21.00146 1.650000  88.02694           2            2       2.482575
1374      1 22.87795 1.669130  86.00274           2            2       2.290095
1375      1 22.84762 1.669136  85.56839           2            2       2.074843
1376      1 23.00000 1.611058  84.19112           2            2       2.141280
1377      1 23.00000 1.649736  84.13471           2            2       2.334474
1378      1 39.12929 1.532643  77.55034           2            2       2.000000
1379      1 37.44104 1.524293  76.20276           2            2       2.000000
1380      2 22.96937 1.701634  95.00000           2            2       2.000000
1381      2 22.73533 1.723921  94.74389           2            2       2.000000
1382      2 23.32471 1.769484  96.07846           2            2       2.000000
1383      2 23.66814 1.777416  97.84241           2            2       2.000000
1384      1 21.89547 1.644560  88.11914           2            2       2.693859
1385      1 21.39280 1.641149  88.07918           2            2       2.607747
1386      1 18.17808 1.642892  82.14441           2            2       2.668890
1387      1 18.90751 1.650000  82.01831           2            2       3.000000
1388      2 26.01193 1.777251 106.45237           2            2       3.000000
1389      2 25.69674 1.814696 107.55963           2            2       2.921225
1390      1 40.46631 1.559005  77.60148           2            2       2.000000
1391      1 38.44515 1.556028  77.68423           2            2       2.000000
1392      2 24.47306 1.653751  91.20475           2            2       1.492834
1393      2 23.47918 1.680171  91.06805           2            2       1.220024
1394      1 38.39746 1.552648  80.00000           2            2       2.667676
1395      1 37.97448 1.560215  80.00000           2            2       2.569075
1396      2 31.78352 1.672959  90.00000           2            2       2.949242
1397      2 29.68131 1.680058  89.99435           2            2       2.104772
1398      2 21.58774 1.664752  94.25655           2            2       2.000000
1399      2 21.74611 1.668553  94.45439           2            2       2.000000
1400      2 39.56900 1.785286 100.43162           2            2       2.871768
1401      2 36.67933 1.780725  98.79017           2            2       2.540949
1402      2 22.92701 1.751046  95.28590           2            2       2.000000
1403      2 23.32934 1.751365  95.29043           2            2       2.000000
1404      2 22.12633 1.717262  98.57916           2            2       2.000000
1405      2 21.67916 1.741393  98.18250           2            2       2.000000
1406      2 36.67388 1.792100 101.28576           2            2       2.222282
1407      2 37.93604 1.785957  99.19929           2            2       2.045160
1408      1 22.22681 1.609068  83.78531           2            2       2.094184
1409      1 22.53893 1.603963  82.01264           2            2       1.123672
1410      1 22.30741 1.605495  82.52858           2            2       2.049112
1411      1 22.36288 1.607182  82.36844           2            2       1.880534
1412      1 22.89974 1.661715  82.59579           2            2       1.203754
1413      1 22.99717 1.655742  82.46376           2            2       2.736910
1414      1 38.89507 1.549257  80.00000           2            2       2.736628
1415      1 40.78953 1.549748  80.00000           2            2       2.000000
1416      1 40.65416 1.529060  79.76092           2            2       2.000000
1417      1 38.54294 1.517248  79.84322           2            2       2.000000
1418      2 25.95383 1.657310  90.00000           2            2       2.000000
1419      2 26.82696 1.673287  90.00000           2            2       2.000000
1420      1 23.80390 1.581527  78.08957           2            2       2.000000
1421      1 23.65244 1.562724  80.53570           2            2       2.000000
1422      2 20.67711 1.781251 102.95093           2            2       2.000000
1423      2 21.79739 1.775584 103.60590           2            2       2.121909
1424      2 22.71938 1.746645  96.87550           2            2       2.000000
1425      2 22.17710 1.741353  96.73801           2            2       2.000000
1426      1 23.09991 1.571812  78.99717           2            2       2.000000
1427      1 24.17851 1.589027  80.55307           2            2       2.000000
1428      2 20.92481 1.803245 105.68609           2            2       2.000000
1429      2 20.97597 1.792646 105.61914           2            2       2.000000
1430      2 21.85630 1.871990 115.62755           2            2       2.000000
1431      2 18.14075 1.859056 111.23519           2            2       2.000000
1432      1 18.00000 1.692242  90.01950           2            2       2.642744
1433      1 18.00000 1.683000  90.92421           2            2       2.387426
1434      1 20.20636 1.738397  93.89068           2            2       1.996638
1435      1 18.15288 1.694969  92.50812           2            2       2.107854
1436      2 22.33622 1.785718 105.05569           2            2       2.253707
1437      2 23.00000 1.742500 105.02867           2            2       2.393837
1438      2 22.08806 1.803132 106.32957           2            2       2.348745
1439      2 23.00000 1.774644 105.96689           2            2       2.312825
1440      2 20.99307 1.782269 104.97003           2            2       2.000000
1441      2 20.65475 1.780791 102.92122           2            2       2.000000
1442      2 21.62455 1.790151 106.32069           2            2       2.490776
1443      2 21.68264 1.818641 105.49698           2            2       2.078082
1444      2 18.00000 1.806827 108.82940           2            2       2.000000
1445      2 18.00000 1.811189 108.80096           2            2       2.000000
1446      2 18.00000 1.811738 108.89732           2            2       2.000000
1447      2 18.00000 1.799779 108.93457           2            2       2.000000
1448      1 19.37421 1.632605  82.00000           2            2       2.805512
1449      1 19.27569 1.623377  82.85132           2            2       1.276858
1450      1 22.95685 1.618686  81.28158           2            2       2.396265
1451      1 22.82968 1.616085  82.58295           2            2       2.663866
1452      2 22.20078 1.769328 105.00058           2            2       2.685484
1453      2 21.68856 1.807029 105.69636           2            2       2.055209
1454      2 20.80319 1.882533 117.46852           2            2       2.000000
1455      2 19.51532 1.879144 112.93298           2            2       2.000000
1456      2 21.96222 1.931263 118.20313           2            2       2.000000
1457      2 20.53409 1.861358 115.39792           2            2       2.000000
1458      1 39.29266 1.567131  80.00000           2            2       2.025479
1459      1 37.87297 1.565366  80.00000           2            2       2.002564
1460      1 37.61338 1.516007  77.03305           2            2       2.000000
1461      1 37.47168 1.549812  76.08252           2            2       2.000000
1462      2 18.61120 1.714143  96.56881           2            2       2.000000
1463      2 17.41263 1.723191  97.35037           2            2       2.000000
1464      1 39.12631 1.562889  76.65949           2            2       2.000000
1465      1 37.06360 1.502609  75.27961           2            2       2.000000
1466      1 41.31830 1.544937  77.05395           2            2       2.000000
1467      1 43.72608 1.592316  77.00103           2            2       2.000000
1468      2 18.00000 1.787733 108.01921           2            2       2.000000
1469      2 18.00000 1.782722 108.04431           2            2       2.000000
1470      2 18.00000 1.797523 108.31646           2            2       2.000000
1471      2 18.00000 1.803527 108.25104           2            2       2.000000
1472      2 29.40983 1.801224 108.15619           2            2       2.385502
1473      2 28.42153 1.829239 107.10819           2            2       2.465575
1474      1 18.00000 1.692913  89.93889           2            2       2.818502
1475      1 18.00000 1.668555  85.60747           2            2       2.081238
1476      2 23.24670 1.652971  93.31028           2            2       1.368978
1477      2 24.48103 1.661277  90.74496           2            2       1.712848
1478      2 29.00000 1.641784  89.42495           2            2       1.330700
1479      2 27.96877 1.673767  89.99503           2            2       1.961069
1480      1 18.02474 1.617192  83.12181           2            2       2.976509
1481      1 18.10682 1.602129  82.41267           2            2       2.319648
1482      2 21.37968 1.701413  96.71073           2            2       2.000000
1483      2 21.35324 1.709731  95.03218           2            2       2.000000
1484      2 20.33882 1.698829  98.57275           2            2       2.000000
1485      2 20.72064 1.705304  99.87372           2            2       2.000000
1486      1 19.04536 1.612910  82.19340           2            2       1.261288
1487      1 18.94596 1.605469  82.03900           2            2       2.765330
1488      2 18.88061 1.804160 104.40682           2            2       2.000000
1489      2 19.85052 1.785062 104.18731           2            2       2.000000
1490      2 30.20095 1.755926 112.28988           2            2       2.317734
1491      2 25.31459 1.787802 115.12766           2            2       1.588114
1492      2 37.18679 1.704877 107.94747           2            2       2.549782
1493      2 32.70765 1.695347 102.81070           2            2       2.795086
1494      2 27.56243 1.908608 128.82812           2            2       2.206276
1495      2 30.42160 1.819296 122.13792           2            2       2.000000
1496      2 26.94514 1.773259 118.15435           2            1       2.183540
1497      2 34.13945 1.774647 120.15197           2            1       2.962415
1498      2 26.22544 1.773664 116.16033           2            2       2.442536
1499      2 28.36315 1.793476 112.72500           2            2       1.991240
1500      2 27.99147 1.825590 120.86039           2            2       3.000000
1501      2 25.49875 1.885543 121.25308           2            2       2.408561
1502      2 30.60523 1.750000 120.00000           2            2       2.758394
1503      2 31.45741 1.874070 128.86744           2            2       2.956297
1504      2 24.82929 1.755967 112.25616           2            2       1.369529
1505      2 24.73942 1.757069 117.29823           2            2       1.392665
1506      2 41.00000 1.750000 116.59435           2            2       2.000000
1507      2 27.83173 1.704028 101.63431           2            2       2.630401
1508      2 23.26006 1.849003 121.41474           2            2       3.000000
1509      2 22.70577 1.841989 122.02495           2            2       3.000000
1510      2 24.04104 1.613491  98.49077           2            2       3.000000
1511      2 20.01391 1.647821 106.50995           2            2       2.869778
1512      2 30.02019 1.758340 112.01050           2            2       1.868212
1513      2 25.15462 1.758398 112.08902           2            2       1.108663
1514      2 30.87072 1.670774 101.62619           2            2       2.907744
1515      2 28.71299 1.665585  98.66176           2            2       2.958010
1516      2 30.71051 1.914186 129.85253           2            2       2.197261
1517      2 29.82748 1.899588 124.26925           2            2       2.048962
1518      2 31.86893 1.750000 118.10290           2            2       2.139196
1519      2 36.54288 1.750000 119.43465           2            2       2.729890
1520      2 21.55636 1.773664 116.16033           2            2       2.000000
1521      2 23.96365 1.792998 116.10262           2            2       1.994679
1522      2 24.14530 1.825590 120.86039           2            2       2.403421
1523      2 25.29840 1.827279 120.99607           2            2       3.000000
1524      2 31.75539 1.775416 121.20467           2            2       2.758394
1525      2 30.62865 1.766975 118.36338           2            2       2.964319
1526      2 23.45456 1.754218 117.38474           2            2       1.617093
1527      2 23.43223 1.754996 119.08756           2            2       1.631144
1528      2 40.50021 1.748103 108.30811           2            2       2.317459
1529      2 34.57671 1.733250 103.66912           2            2       2.501224
1530      2 22.27697 1.849950 121.78648           2            2       3.000000
1531      2 21.96379 1.849601 122.33343           2            2       3.000000
1532      2 24.00168 1.606066  99.96173           2            2       3.000000
1533      2 20.02776 1.611434 103.17552           2            2       2.983042
1534      2 28.70446 1.762887 113.90198           2            2       2.323351
1535      2 26.77411 1.755938 112.28768           2            2       1.428289
1536      2 25.53941 1.787052 115.42828           2            2       1.992889
1537      2 25.30021 1.765258 114.33002           2            2       1.562804
1538      2 31.19446 1.726279 110.71471           2            2       1.794825
1539      2 40.79406 1.735851 109.88933           2            2       2.305349
1540      2 33.55336 1.699793 103.84167           2            2       2.576449
1541      2 30.30420 1.703202 103.03440           2            2       2.754645
1542      2 30.95805 1.906821 128.85668           2            2       2.633855
1543      2 29.42969 1.910987 129.93567           2            2       2.200588
1544      2 30.52085 1.784049 120.64418           2            2       2.499108
1545      2 30.61044 1.783914 120.54959           2            2       2.056870
1546      2 26.74066 1.863883 120.20260           2            2       2.247795
1547      2 26.68038 1.812259 118.27766           2            2       2.239634
1548      2 37.05619 1.750150 118.20656           2            2       2.092830
1549      2 28.82528 1.815347 120.39976           2            2       2.967300
1550      2 25.88375 1.767077 114.13315           2            2       2.225731
1551      2 25.48652 1.762461 115.53162           2            2       2.178308
1552      2 29.88302 1.779049 112.43851           2            2       2.065752
1553      2 26.95764 1.780731 112.95792           2            2       2.263245
1554      2 26.49093 1.872517 120.89840           2            2       2.443538
1555      2 27.55880 1.801224 119.05038           2            2       2.744994
1556      2 22.90888 1.876898 121.27783           2            2       2.630137
1557      2 24.20162 1.775580 120.58942           2            2       2.113843
1558      2 31.68977 1.765690 120.08294           2            2       2.821727
1559      2 30.24260 1.759772 118.38236           2            2       2.312528
1560      2 31.34684 1.823545 126.46094           2            2       2.938801
1561      2 30.57734 1.868923 125.06426           2            2       2.050619
1562      2 25.12459 1.771510 113.20712           2            2       1.457758
1563      2 25.50903 1.772190 114.09766           2            2       2.193310
1564      2 25.47866 1.858265 117.57457           2            2       2.028571
1565      2 25.10051 1.830596 118.42416           2            2       1.455602
1566      2 38.52365 1.765836 118.53325           2            2       2.177896
1567      2 38.71216 1.770278 117.29188           2            2       2.252382
1568      2 25.71752 1.673491 103.70618           2            2       2.668949
1569      2 32.25962 1.703346 102.68624           2            2       2.723953
1570      2 23.47007 1.842906 121.14253           2            2       3.000000
1571      2 24.40881 1.779547 118.74004           2            2       2.736298
1572      2 24.44301 1.873368 121.82962           2            2       2.722161
1573      2 22.90634 1.755902 120.02116           2            2       2.971574
1574      2 24.00749 1.617655 100.94136           2            2       2.871016
1575      2 30.68670 1.644517 100.00442           2            2       2.964050
1576      2 20.18445 1.701284 104.57825           2            2       2.653721
1577      2 25.44721 1.658910 104.54879           2            2       2.859097
1578      2 30.00203 1.759324 112.00038           2            2       1.572036
1579      2 30.18830 1.758382 112.10074           2            2       1.387489
1580      2 24.24403 1.622297  99.98254           2            2       2.941929
1581      2 24.52188 1.623278  97.58296           2            2       2.973569
1582      2 29.72196 1.918859 129.99162           2            2       2.041376
1583      2 29.90658 1.913252 129.23222           2            2       2.024720
1584      2 37.08474 1.750000 118.07381           2            2       2.133964
1585      2 39.08886 1.750000 119.02910           2            2       2.702457
1586      2 22.65857 1.784994 113.71452           2            2       1.760038
1587      2 22.88910 1.792533 116.15777           2            2       1.996646
1588      2 24.63474 1.829757 120.98051           2            2       2.644094
1589      2 24.92036 1.796415 120.98845           2            2       2.475892
1590      2 31.78384 1.750000 120.00000           2            2       2.941627
1591      2 31.62796 1.762389 118.54873           2            2       2.998441
1592      2 22.97619 1.825718 121.23691           2            2       2.397284
1593      2 22.97736 1.839699 120.43155           2            2       2.382705
1594      2 40.97301 1.749405 109.90878           2            2       2.176317
1595      2 41.00000 1.750000 115.80698           2            2       2.000000
1596      2 21.72151 1.849998 122.11968           2            2       3.000000
1597      2 21.54455 1.849980 122.60991           2            2       3.000000
1598      2 24.00031 1.607939 100.61824           2            2       2.877743
1599      2 24.04090 1.603226  99.60553           2            2       3.000000
1600      2 29.50915 1.770663 112.47112           2            2       2.303041
1601      2 29.24627 1.758038 112.39531           2            2       2.323003
1602      2 25.34140 1.786997 115.02536           2            2       1.999530
1603      2 25.31416 1.771837 114.90609           2            2       1.585183
1604      2 40.36624 1.722396 109.34902           2            2       2.281963
1605      2 33.74959 1.701387 107.02541           2            2       2.561638
1606      2 32.86733 1.697421 103.01762           2            2       2.600217
1607      2 30.40320 1.696365 102.81598           2            2       2.791660
1608      2 29.68749 1.909198 129.67913           2            2       2.088680
1609      2 28.99281 1.909105 129.87486           2            2       2.206119
1610      2 30.47525 1.801368 121.09426           2            2       2.328469
1611      2 30.35052 1.860292 123.72135           2            2       2.002784
1612      2 26.68435 1.819535 118.33269           2            2       1.975675
1613      2 26.60748 1.793174 118.16508           2            2       2.002076
1614      2 33.17415 1.750150 118.29958           2            2       2.218599
1615      2 32.29016 1.754956 120.09881           2            2       2.967300
1616      2 26.05011 1.773115 115.08932           2            2       2.392179
1617      2 25.84628 1.772731 115.82817           2            2       2.381164
1618      2 29.62009 1.787933 112.53637           2            2       2.008245
1619      2 27.43906 1.785277 112.74080           2            2       2.155182
1620      2 28.49340 1.817572 120.81580           2            2       2.969233
1621      2 27.47424 1.843420 120.42041           2            2       2.765063
1622      2 25.03627 1.874519 121.24497           2            2       2.630137
1623      2 24.82540 1.796332 120.90159           2            2       2.195964
1624      2 30.49395 1.756221 119.11712           2            2       2.619987
1625      2 30.60755 1.757132 118.56557           2            2       2.918113
1626      2 31.19432 1.889104 129.15735           2            2       2.889485
1627      2 31.36347 1.869323 127.50741           2            2       2.939727
1628      2 25.02725 1.757154 112.20081           2            2       1.264234
1629      2 25.05857 1.764484 113.23435           2            2       1.517912
1630      2 24.41755 1.774775 117.39898           2            2       2.233720
1631      2 24.58220 1.769933 117.70870           2            2       1.475906
1632      2 40.10614 1.760175 117.65105           2            2       2.032883
1633      2 40.17419 1.763029 116.97450           2            2       2.046651
1634      2 27.18687 1.679515 102.87280           2            2       2.667229
1635      2 30.42437 1.699354 100.17687           2            2       2.819934
1636      2 23.14140 1.849307 121.65873           2            2       3.000000
1637      2 24.17864 1.867410 121.68431           2            2       2.954417
1638      2 22.83210 1.867140 121.83588           2            2       2.826251
1639      2 23.08362 1.848553 121.42112           2            2       3.000000
1640      2 24.07997 1.619810  98.54302           2            2       2.958410
1641      2 26.94779 1.647807  99.59222           2            2       2.935157
1642      2 20.10103 1.619128 104.30371           2            2       2.870895
1643      2 22.78940 1.641870 104.27006           2            2       2.869833
1644      2 28.40433 1.787379 112.17373           2            2       1.878251
1645      2 30.00336 1.758355 112.00710           2            2       1.750809
1646      2 25.13612 1.764140 113.08972           2            2       1.168856
1647      2 25.13947 1.767186 112.22603           2            2       1.443674
1648      2 31.74382 1.677820 102.02206           2            2       2.826036
1649      2 30.79626 1.668478 100.54398           2            2       2.955300
1650      2 28.39311 1.685462  99.54012           2            2       2.774562
1651      2 27.93982 1.694642  99.70933           2            2       2.772027
1652      2 31.03409 1.886109 129.34947           2            2       2.499626
1653      2 30.68435 1.915000 129.96643           2            2       2.108638
1654      2 30.10822 1.841908 124.07072           2            2       2.467548
1655      2 31.34750 1.868127 123.17221           2            2       2.585942
1656      2 31.76180 1.751688 119.20531           2            2       2.149610
1657      2 30.97693 1.755333 118.23778           2            2       2.938616
1658      2 37.58863 1.754217 117.89720           2            2       2.535154
1659      2 37.99791 1.760710 118.66833           2            2       2.611222
1660      2 21.65480 1.755938 116.31196           2            2       1.800122
1661      2 22.82975 1.765137 116.66991           2            2       1.482722
1662      2 25.01517 1.788239 115.38252           2            2       1.735664
1663      2 23.81280 1.767121 116.16435           2            2       1.655684
1664      2 23.97568 1.840039 120.97549           2            2       2.598207
1665      2 24.14904 1.824901 120.80572           2            2       2.225149
1666      2 25.06294 1.828391 120.99827           2            2       3.000000
1667      2 25.14007 1.829529 120.99658           2            2       3.000000
1668      2 30.72280 1.779325 120.75166           2            2       2.519592
1669      2 30.91643 1.781139 120.79453           2            2       2.111887
1670      2 31.01030 1.764166 119.37302           2            2       2.970983
1671      2 30.60546 1.754796 118.80594           2            2       2.907542
1672      2 24.62205 1.756509 117.36872           2            2       1.451337
1673      2 23.74583 1.756772 117.32652           2            2       1.537505
1674      2 23.32784 1.754439 119.44121           2            2       1.893428
1675      2 23.41114 1.755142 119.19292           2            2       2.007845
1676      2 39.82559 1.706741 108.01260           2            2       2.487781
1677      2 36.83976 1.742850 106.42104           2            2       2.541785
1678      2 33.78930 1.681100 102.52311           2            2       2.813775
1679      2 34.16200 1.705682 103.06777           2            2       2.562687
1680      2 22.58004 1.849507 121.56094           2            2       3.000000
1681      2 22.85172 1.853373 121.73784           2            2       2.922511
1682      2 23.25493 1.847530 122.06261           2            2       3.000000
1683      2 22.34320 1.870340 121.45823           2            2       2.836055
1684      2 24.00627 1.607787 100.78343           2            2       2.880759
1685      2 24.00120 1.603091 100.20941           2            2       3.000000
1686      2 20.15666 1.620109 103.39335           2            2       2.766441
1687      2 23.36031 1.610647 100.64226           2            2       2.997524
1688      2 28.98624 1.758618 113.50155           2            2       2.320201
1689      2 29.71317 1.763259 113.21581           2            2       2.181057
1690      2 27.39412 1.764138 112.32321           2            2       1.924632
1691      2 24.91225 1.755960 112.27757           2            2       1.397468
1692      2 25.52675 1.783381 115.34718           2            2       2.191429
1693      2 25.52313 1.786532 114.53468           2            2       2.100177
1694      2 25.82235 1.766626 114.18710           2            2       2.075321
1695      2 25.87941 1.765464 114.14438           2            2       1.626369
1696      2 30.36136 1.758530 111.63546           2            2       1.263216
1697      2 30.45117 1.758539 111.95041           2            2       1.067909
1698      2 40.56451 1.748015 109.75874           2            2       2.310751
1699      2 40.50172 1.744974 111.16968           2            2       2.294259
1700      2 30.31578 1.701566 103.74353           2            2       2.576490
1701      2 33.29317 1.696412 103.25035           2            2       2.679664
1702      2 30.63894 1.680489 102.00455           2            2       2.938687
1703      2 29.79110 1.703317 102.59217           2            2       2.654792
1704      2 31.20567 1.877732 127.16138           2            2       2.731368
1705      2 30.89922 1.909639 129.01318           2            2       2.222590
1706      2 29.66922 1.909188 129.19449           2            2       2.432355
1707      2 30.59563 1.910672 129.23271           2            2       2.497548
1708      2 30.55496 1.779136 120.60094           2            2       2.671238
1709      2 31.57139 1.767485 120.15805           2            2       2.576910
1710      2 30.57794 1.783953 120.61356           2            2       2.341999
1711      2 30.71773 1.767140 120.34440           2            2       2.117121
1712      2 26.73448 1.816197 119.62276           2            2       2.247037
1713      2 26.69932 1.839941 119.95665           2            2       2.244654
1714      2 25.65909 1.848420 117.63171           2            2       2.128574
1715      2 26.34816 1.830317 117.75701           2            2       2.068834
1716      2 37.63810 1.750085 118.11418           2            2       2.073224
1717      2 37.76536 1.763582 117.86159           2            2       2.145114
1718      2 28.25520 1.816547 120.69912           2            2       2.997951
1719      2 27.93143 1.805445 119.48461           2            2       2.911312
1720      2 25.49285 1.770124 114.16392           2            2       2.159033
1721      2 25.55051 1.772740 114.25428           2            2       2.175276
1722      2 25.54245 1.763215 115.10813           2            2       2.219650
1723      2 25.75831 1.766547 115.45845           2            2       2.203962
1724      2 29.98149 1.773181 112.34872           2            2       1.947495
1725      2 29.89147 1.760030 112.40919           2            2       2.262292
1726      2 26.77868 1.780089 113.15464           2            2       2.219186
1727      2 28.37796 1.766946 113.23554           2            2       2.314175
1728      2 26.19932 1.885119 121.06505           2            2       2.423291
1729      2 27.26629 1.828276 120.87229           2            2       2.637202
1730      2 27.63503 1.806227 120.42357           2            2       2.974006
1731      2 26.89989 1.812919 119.84145           2            2       2.347942
1732      2 22.75900 1.859717 121.28453           2            2       2.654076
1733      2 22.77100 1.865160 121.52737           2            2       2.739000
1734      2 23.82668 1.783609 120.92154           2            2       2.591292
1735      2 24.18627 1.794827 120.91970           2            2       2.611847
1736      2 30.94537 1.759647 120.00939           2            2       2.766612
1737      2 31.49070 1.773521 120.20971           2            2       2.777165
1738      2 31.56724 1.753327 118.26569           2            2       2.232836
1739      2 30.44408 1.761068 118.37063           2            2       2.571274
1740      2 31.19926 1.848845 125.07786           2            2       2.496190
1741      2 31.19022 1.842812 125.97393           2            2       2.151335
1742      2 30.57535 1.825449 124.95278           2            2       2.016950
1743      2 30.70256 1.861980 126.41841           2            2       2.927187
1744      2 25.05788 1.763987 113.06967           2            2       1.412566
1745      2 25.15129 1.761519 112.83519           2            2       1.289315
1746      2 25.13709 1.772045 114.06794           2            2       1.624366
1747      2 25.32920 1.771817 114.00483           2            2       1.528331
1748      2 25.66668 1.798580 117.93329           2            2       2.037042
1749      2 25.01277 1.788586 117.84935           2            2       2.191108
1750      2 25.04794 1.803207 118.33297           2            2       1.431346
1751      2 25.42646 1.836592 118.37760           2            2       1.841990
1752      2 37.20708 1.762921 118.40174           2            2       2.136830
1753      2 38.10894 1.752863 119.20146           2            2       2.499388
1754      2 38.64444 1.768235 117.79227           2            2       2.230742
1755      2 38.11299 1.766888 118.13490           2            2       2.240757
1756      2 24.82539 1.603501 101.03826           2            2       2.996186
1757      2 26.62434 1.690262 103.18092           2            2       2.649406
1758      2 33.72245 1.712905 103.27609           2            2       2.525884
1759      2 32.51647 1.695735 102.78486           2            2       2.736647
1760      2 23.88194 1.829971 121.04172           2            2       2.684335
1761      2 23.31964 1.846290 121.24804           2            2       3.000000
1762      2 23.91239 1.774983 119.08180           2            2       2.425503
1763      2 24.98300 1.789680 118.43617           2            2       1.469384
1764      2 24.51144 1.852664 121.31026           2            2       2.906269
1765      2 24.05331 1.872561 121.47108           2            2       2.808027
1766      2 22.90689 1.819550 120.77544           2            2       2.636719
1767      2 23.14764 1.815514 120.33766           2            2       2.996717
1768      2 24.00189 1.614075 100.24530           2            2       2.880483
1769      2 24.00240 1.609418 100.07837           2            2       2.885693
1770      2 30.71516 1.650189 101.14128           2            2       2.913452
1771      2 30.64243 1.653876 102.58389           2            2       2.919526
1772      2 20.06843 1.657132 105.58049           2            2       2.724121
1773      2 20.91437 1.644751 101.06799           2            2       2.801992
1774      2 25.51205 1.660761 104.32146           2            2       2.748971
1775      2 26.84481 1.691510 102.59518           2            2       2.680375
1776      1 25.91924 1.611462 102.09322           2            2       3.000000
1777      1 26.00000 1.584782 105.05560           2            2       3.000000
1778      1 18.23354 1.792378 137.85974           2            2       3.000000
1779      1 18.14770 1.802257 149.93585           2            2       3.000000
1780      1 26.00000 1.656320 111.93301           2            2       3.000000
1781      1 26.00000 1.600762 105.44826           2            2       3.000000
1782      1 25.90228 1.678658 104.95483           2            2       3.000000
1783      1 25.54087 1.678201 109.90047           2            2       3.000000
1784      1 22.39251 1.655630 121.20517           2            2       3.000000
1785      1 21.30540 1.694952 133.76473           2            2       3.000000
1786      1 26.00000 1.622771 109.95971           2            2       3.000000
1787      1 26.00000 1.583889 110.54538           2            2       3.000000
1788      1 21.33459 1.729045 131.52927           2            2       3.000000
1789      1 22.97865 1.664622 114.46542           2            2       3.000000
1790      1 21.01245 1.747773 127.90284           2            2       3.000000
1791      1 21.05606 1.730113 152.09436           2            2       3.000000
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1793      1 24.26594 1.719365 114.51154           2            2       3.000000
1794      1 26.00000 1.632377 111.94666           2            2       3.000000
1795      1 26.00000 1.642572 111.86817           2            2       3.000000
1796      1 25.96701 1.654875 104.75871           2            2       3.000000
1797      1 26.00000 1.627567 107.48266           2            2       3.000000
1798      1 20.59005 1.736276 130.92714           2            2       3.000000
1799      1 24.49737 1.737056 132.52701           2            2       3.000000
1800      1 25.99189 1.580968 102.00338           2            2       3.000000
1801      1 25.90843 1.607128 103.02686           2            2       3.000000
1802      1 18.17788 1.821566 142.10247           2            2       3.000000
1803      1 18.11250 1.827730 152.72054           2            2       3.000000
1804      1 26.00000 1.656504 111.99305           2            2       3.000000
1805      1 26.00000 1.637725 111.20896           2            2       3.000000
1806      1 25.90228 1.669701 104.58578           2            2       3.000000
1807      1 25.54087 1.668709 104.75496           2            2       3.000000
1808      1 20.52099 1.668642 124.70478           2            2       3.000000
1809      1 20.87115 1.690614 129.76914           2            2       3.000000
1810      1 26.00000 1.622418 110.67634           2            2       3.000000
1811      1 26.00000 1.643332 110.82470           2            2       3.000000
1812      1 21.76883 1.733383 135.52486           2            2       3.000000
1813      1 20.89149 1.748313 133.57379           2            2       3.000000
1814      1 20.94194 1.812963 138.73062           2            2       3.000000
1815      1 20.98902 1.807340 155.87209           2            2       3.000000
1816      1 18.36748 1.745644 133.66559           2            2       3.000000
1817      1 21.05111 1.753266 133.85243           2            2       3.000000
1818      1 26.00000 1.632193 111.88661           2            2       3.000000
1819      1 26.00000 1.641098 111.81834           2            2       3.000000
1820      1 25.99865 1.640741 104.80854           2            2       3.000000
1821      1 26.00000 1.618573 104.92864           2            2       3.000000
1822      1 22.84636 1.757442 133.36509           2            2       3.000000
1823      1 19.88565 1.763343 133.95267           2            2       3.000000
1824      1 25.93038 1.610086 102.38745           2            2       3.000000
1825      1 25.89782 1.664463 102.78197           2            2       3.000000
1826      1 26.00000 1.602025 104.89935           2            2       3.000000
1827      1 25.96879 1.632896 104.98892           2            2       3.000000
1828      1 18.33502 1.771043 137.04496           2            2       3.000000
1829      1 21.70075 1.789555 137.76779           2            2       3.000000
1830      1 18.22254 1.801746 141.16627           2            2       3.000000
1831      1 20.37560 1.787195 151.97586           2            2       3.000000
1832      1 26.00000 1.644141 111.94254           2            2       3.000000
1833      1 26.00000 1.643355 111.60055           2            2       3.000000
1834      1 26.00000 1.623707 105.03746           2            2       3.000000
1835      1 26.00000 1.610636 105.42353           2            2       3.000000
1836      1 25.94383 1.629491 104.83907           2            2       3.000000
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1838      1 25.65323 1.664940 110.92217           2            2       3.000000
1839      1 25.78386 1.655646 110.21734           2            2       3.000000
1840      1 19.17614 1.666194 124.80587           2            2       3.000000
1841      1 21.20742 1.707508 121.86433           2            2       3.000000
1842      1 21.63306 1.754174 133.78395           2            2       3.000000
1843      1 21.00960 1.714193 131.86673           2            2       3.000000
1844      1 26.00000 1.624390 110.00864           2            2       3.000000
1845      1 26.00000 1.638836 110.97048           2            2       3.000000
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1849      1 21.77225 1.732951 132.90488           2            2       3.000000
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1853      1 19.72572 1.746529 129.36377           2            2       3.000000
1854      1 21.34402 1.746516 144.30226           2            2       3.000000
1855      1 21.32210 1.751118 149.29111           2            2       3.000000
1856      1 19.26293 1.741014 132.57927           2            2       3.000000
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1862      1 26.00000 1.640125 111.53949           2            2       3.000000
1863      1 26.00000 1.649178 111.91436           2            2       3.000000
1864      1 25.96773 1.603404 105.03191           2            2       3.000000
1865      1 25.98994 1.644199 105.03607           2            2       3.000000
1866      1 26.00000 1.628909 106.87593           2            2       3.000000
1867      1 25.61723 1.628019 108.26592           2            2       3.000000
1868      1 21.03091 1.718180 133.46676           2            2       3.000000
1869      1 20.95108 1.708581 131.27485           2            2       3.000000
1870      1 22.98022 1.724983 132.94066           2            2       3.000000
1871      1 23.42604 1.739991 133.48548           2            2       3.000000
1872      1 25.99919 1.568543 102.00012           2            2       3.000000
1873      1 25.93476 1.579893 102.13465           2            2       3.000000
1874      1 20.32772 1.782714 154.61845           2            2       3.000000
1875      1 19.47219 1.793824 160.93535           2            2       3.000000
1876      1 26.00000 1.659557 111.99910           2            2       3.000000
1877      1 26.00000 1.657820 111.95611           2            2       3.000000
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1879      1 25.49896 1.683950 104.84682           2            2       3.000000
1880      1 19.13750 1.716521 127.64232           2            2       3.000000
1881      1 20.19073 1.680762 125.41855           2            2       3.000000
1882      1 26.00000 1.623303 110.81746           2            2       3.000000
1883      1 26.00000 1.631856 110.80434           2            2       3.000000
1884      1 21.84065 1.747479 136.51665           2            2       3.000000
1885      1 20.87167 1.782453 137.85262           2            2       3.000000
1886      1 21.50172 1.809871 152.39474           2            2       3.000000
1887      1 21.52129 1.803677 160.63941           2            2       3.000000
1888      1 18.31459 1.745602 133.55469           2            2       3.000000
1889      1 19.52975 1.751038 133.84303           2            2       3.000000
1890      1 26.00000 1.631547 111.58862           2            2       3.000000
1891      1 26.00000 1.635905 111.57108           2            2       3.000000
1892      1 26.00000 1.616975 104.84622           2            2       3.000000
1893      1 26.00000 1.627880 104.92057           2            2       3.000000
1894      1 21.76815 1.764160 133.88863           2            2       3.000000
1895      1 21.23842 1.763847 133.93787           2            2       3.000000
1896      1 25.93038 1.608808 102.08396           2            2       3.000000
1897      1 25.91957 1.610488 102.17495           2            2       3.000000
1898      1 25.91852 1.621231 104.98679           2            2       3.000000
1899      1 25.56566 1.642392 104.98808           2            2       3.000000
1900      1 18.74491 1.801983 138.03453           2            2       3.000000
1901      1 21.70470 1.787614 137.85825           2            2       3.000000
1902      1 20.08997 1.801478 151.41729           2            2       3.000000
1903      1 18.12074 1.807576 152.56767           2            2       3.000000
1904      1 26.00000 1.650125 111.93967           2            2       3.000000
1905      1 26.00000 1.652674 111.91916           2            2       3.000000
1906      1 25.98618 1.663632 105.12211           2            2       3.000000
1907      1 25.98211 1.627818 105.42863           2            2       3.000000
1908      1 25.74863 1.668770 104.77414           2            2       3.000000
1909      1 25.95977 1.642098 104.96676           2            2       3.000000
1910      1 25.65323 1.657570 109.93123           2            2       3.000000
1911      1 25.78386 1.643111 109.91001           2            2       3.000000
1912      1 21.41243 1.675562 121.63918           2            2       3.000000
1913      1 22.77789 1.661415 120.74821           2            2       3.000000
1914      1 21.14016 1.713133 133.73589           2            2       3.000000
1915      1 21.41350 1.719900 133.88603           2            2       3.000000
1916      1 26.00000 1.622701 109.98269           2            2       3.000000
1917      1 26.00000 1.622468 110.40085           2            2       3.000000
1918      1 26.00000 1.618867 110.77739           2            2       3.000000
1919      1 26.00000 1.600905 110.07495           2            2       3.000000
1920      1 21.39137 1.730636 131.90259           2            2       3.000000
1921      1 21.05198 1.729719 131.87756           2            2       3.000000
1922      1 23.45530 1.677672 114.47048           2            2       3.000000
1923      1 23.69484 1.637524 113.90506           2            2       3.000000
1924      1 20.60122 1.738717 128.11416           2            2       3.000000
1925      1 20.81158 1.741193 128.76384           2            2       3.000000
1926      1 19.99357 1.792833 152.43563           2            2       3.000000
1927      1 20.07445 1.810427 152.21714           2            2       3.000000
1928      1 18.90404 1.743589 133.28133           2            2       3.000000
1929      1 19.78323 1.747962 133.93653           2            2       3.000000
1930      1 24.29121 1.711460 113.37285           2            2       3.000000
1931      1 25.31153 1.685482 113.45122           2            2       3.000000
1932      1 26.00000 1.639251 111.92700           2            2       3.000000
1933      1 26.00000 1.654784 111.93315           2            2       3.000000
1934      1 26.00000 1.641209 111.85649           2            2       3.000000
1935      1 26.00000 1.643167 111.89423           2            2       3.000000
1936      1 25.96650 1.630730 104.79055           2            2       3.000000
1937      1 25.95090 1.649867 104.79103           2            2       3.000000
1938      1 26.00000 1.613574 107.01226           2            2       3.000000
1939      1 26.00000 1.627532 106.69053           2            2       3.000000
1940      1 20.84861 1.726606 131.76807           2            2       3.000000
1941      1 20.80179 1.721476 131.04227           2            2       3.000000
1942      1 23.71264 1.742901 132.80710           2            2       3.000000
1943      1 24.06387 1.737313 133.16660           2            2       3.000000
1944      1 25.95774 1.624140 102.23345           2            2       3.000000
1945      1 25.90935 1.644078 102.27777           2            2       3.000000
1946      1 25.95500 1.592529 102.87455           2            2       3.000000
1947      1 25.90883 1.607734 102.30577           2            2       3.000000
1948      1 19.29700 1.817271 141.91780           2            2       3.000000
1949      1 18.30177 1.808765 140.29202           2            2       3.000000
1950      1 19.87267 1.817231 152.37191           2            2       3.000000
1951      1 18.13782 1.819728 151.27853           2            2       3.000000
1952      1 26.00000 1.656465 111.94997           2            2       3.000000
1953      1 26.00000 1.648143 111.95011           2            2       3.000000
1954      1 26.00000 1.622703 111.21619           2            2       3.000000
1955      1 26.00000 1.640606 111.03688           2            2       3.000000
1956      1 25.42724 1.683502 104.75964           2            2       3.000000
1957      1 25.79519 1.669039 104.59393           2            2       3.000000
1958      1 25.49359 1.673665 104.70471           2            2       3.000000
1959      1 25.52434 1.668931 104.76832           2            2       3.000000
1960      1 20.90879 1.700996 126.49024           2            2       3.000000
1961      1 20.78175 1.734092 125.11763           2            2       3.000000
1962      1 21.28910 1.708291 130.98634           2            2       3.000000
1963      1 21.21073 1.716497 130.87113           2            2       3.000000
1964      1 26.00000 1.624950 111.00492           2            2       3.000000
1965      1 26.00000 1.594776 110.64093           2            2       3.000000
1966      1 26.00000 1.626503 110.81876           2            2       3.000000
1967      1 26.00000 1.624576 110.80312           2            2       3.000000
1968      1 21.65691 1.729099 134.84266           2            2       3.000000
1969      1 21.76073 1.735810 135.34668           2            2       3.000000
1970      1 21.02899 1.748524 133.87884           2            2       3.000000
1971      1 21.28224 1.748951 133.66258           2            2       3.000000
1972      1 20.38805 1.777971 137.78503           2            2       3.000000
1973      1 21.02221 1.739950 135.69338           2            2       3.000000
1974      1 20.10224 1.816868 153.95995           2            2       3.000000
1975      1 21.29197 1.800200 155.24267           2            2       3.000000
1976      1 20.53100 1.746470 133.64471           2            2       3.000000
1977      1 18.97697 1.759091 133.90361           2            2       3.000000
1978      1 20.92496 1.752531 133.61871           2            2       3.000000
1979      1 21.28253 1.761773 133.90347           2            2       3.000000
1980      1 26.00000 1.633195 111.88375           2            2       3.000000
1981      1 26.00000 1.633945 111.93070           2            2       3.000000
1982      1 26.00000 1.641601 111.83092           2            2       3.000000
1983      1 26.00000 1.641132 111.84171           2            2       3.000000
1984      1 25.99917 1.638218 104.81002           2            2       3.000000
1985      1 25.99994 1.627483 104.88199           2            2       3.000000
1986      1 26.00000 1.610225 104.93638           2            2       3.000000
1987      1 26.00000 1.617390 105.01390           2            2       3.000000
1988      1 23.36504 1.744319 133.45249           2            2       3.000000
1989      1 23.42173 1.755467 133.47861           2            2       3.000000
1990      1 18.82678 1.746416 133.74701           2            2       3.000000
1991      1 18.94093 1.746411 133.67666           2            2       3.000000
1992      1 25.92168 1.611452 102.36315           2            2       3.000000
1993      1 25.94015 1.596813 102.32044           2            2       3.000000
1994      1 25.99964 1.610126 102.68691           2            2       3.000000
1995      1 25.90783 1.623113 102.55569           2            2       3.000000
1996      1 25.99119 1.618348 104.94582           2            2       3.000000
1997      1 25.99315 1.609401 104.85493           2            2       3.000000
1998      1 25.98867 1.621671 105.31397           2            2       3.000000
1999      1 25.97621 1.614484 104.99940           2            2       3.000000
2000      1 18.74359 1.789143 138.20287           2            2       3.000000
2001      1 20.32377 1.774207 138.14316           2            2       3.000000
2002      1 21.39405 1.792933 137.83241           2            2       3.000000
2003      1 21.83832 1.758959 137.79299           2            2       3.000000
2004      1 18.20634 1.807406 141.79943           2            2       3.000000
2005      1 18.53283 1.750910 142.54518           2            2       3.000000
2006      1 20.43848 1.805803 153.14949           2            2       3.000000
2007      1 20.79627 1.796538 152.47367           2            2       3.000000
2008      1 26.00000 1.634894 111.94632           2            2       3.000000
2009      1 26.00000 1.639524 111.94559           2            2       3.000000
2010      1 26.00000 1.643017 111.72024           2            2       3.000000
2011      1 26.00000 1.641849 111.68269           2            2       3.000000
2012      1 26.00000 1.621167 104.94770           2            2       3.000000
2013      1 25.99290 1.638075 105.03652           2            2       3.000000
2014      1 26.00000 1.609370 105.40731           2            2       3.000000
2015      1 26.00000 1.608283 105.35969           2            2       3.000000
2016      1 25.95174 1.629442 104.83535           2            2       3.000000
2017      1 25.98226 1.629225 104.83843           2            2       3.000000
2018      1 25.47065 1.680218 104.80728           2            2       3.000000
2019      1 25.28943 1.686033 104.77216           2            2       3.000000
2020      1 25.69607 1.662978 110.93051           2            2       3.000000
2021      1 25.56187 1.675185 110.62172           2            2       3.000000
2022      1 25.83402 1.624560 110.10589           2            2       3.000000
2023      1 25.89555 1.626179 110.07402           2            2       3.000000
2024      1 19.03556 1.682594 127.42746           2            2       3.000000
2025      1 18.63429 1.669354 126.08830           2            2       3.000000
2026      1 20.70088 1.688380 121.88980           2            2       3.000000
2027      1 20.74144 1.694439 122.81303           2            2       3.000000
2028      1 21.69589 1.755476 133.87050           2            2       3.000000
2029      1 21.63598 1.748106 133.25903           2            2       3.000000
2030      1 21.23266 1.719913 131.56748           2            2       3.000000
2031      1 21.00830 1.723587 131.92971           2            2       3.000000
2032      1 25.96295 1.623812 109.99674           2            2       3.000000
2033      1 26.00000 1.624099 109.97840           2            2       3.000000
2034      1 26.00000 1.632983 111.15781           2            2       3.000000
2035      1 26.00000 1.640745 110.91965           2            2       3.000000
2036      1 26.00000 1.629727 111.27565           2            2       3.000000
2037      1 26.00000 1.624134 111.53121           2            2       3.000000
2038      1 25.96479 1.623938 109.98426           2            2       3.000000
2039      1 25.99495 1.593321 110.16817           2            2       3.000000
2040      1 20.95274 1.730333 132.11649           2            2       3.000000
2041      1 20.97817 1.721057 132.05479           2            2       3.000000
2042      1 21.67447 1.719780 132.26256           2            2       3.000000
2043      1 21.56895 1.699315 133.10761           2            2       3.000000
2044      1 23.64794 1.681394 114.47946           2            2       3.000000
2045      1 24.19637 1.697421 114.48239           2            2       3.000000
2046      1 24.28483 1.650726 113.77420           2            2       3.000000
2047      1 24.69311 1.667383 112.98255           2            2       3.000000
2048      1 18.86226 1.746277 128.70576           2            2       3.000000
2049      1 18.42348 1.735461 126.79817           2            2       3.000000
2050      1 20.39408 1.747714 128.14811           2            2       3.000000
2051      1 20.21701 1.715820 129.46654           2            2       3.000000
2052      1 21.33018 1.747987 147.29619           2            2       3.000000
2053      1 19.52894 1.817917 142.55916           2            2       3.000000
2054      1 19.36434 1.808350 150.51648           2            2       3.000000
2055      1 21.13153 1.739457 150.37757           2            2       3.000000
2056      1 19.01287 1.742062 133.77992           2            2       3.000000
2057      1 18.46909 1.741925 133.01710           2            2       3.000000
2058      1 21.57211 1.751067 133.84506           2            2       3.000000
2059      1 21.68012 1.749118 133.95509           2            2       3.000000
2060      1 24.46976 1.663341 113.07719           2            2       3.000000
2061      1 25.12791 1.668537 112.55546           2            2       3.000000
2062      1 25.98637 1.668951 112.24970           2            2       3.000000
2063      1 25.95198 1.661712 112.09862           2            2       3.000000
2064      1 26.00000 1.633442 111.82182           2            2       3.000000
2065      1 26.00000 1.633020 111.86319           2            2       3.000000
2066      1 26.00000 1.633887 111.87813           2            2       3.000000
2067      1 26.00000 1.643421 111.93998           2            2       3.000000
2068      1 26.00000 1.640535 111.55597           2            2       3.000000
2069      1 26.00000 1.626483 111.35706           2            2       3.000000
2070      1 26.00000 1.645990 111.92249           2            2       3.000000
2071      1 26.00000 1.643892 111.88453           2            2       3.000000
2072      1 25.97731 1.617817 104.95078           2            2       3.000000
2073      1 25.95501 1.626449 104.87960           2            2       3.000000
2074      1 25.99672 1.626580 105.03720           2            2       3.000000
2075      1 25.99235 1.606474 104.95429           2            2       3.000000
2076      1 25.97445 1.628855 108.09001           2            2       3.000000
2077      1 25.77756 1.628205 107.37870           2            2       3.000000
2078      1 25.72200 1.628470 107.21895           2            2       3.000000
2079      1 25.76563 1.627839 108.10736           2            2       3.000000
2080      1 21.01685 1.724268 133.03352           2            2       3.000000
2081      1 21.68237 1.732383 133.04394           2            2       3.000000
2082      1 21.28597 1.726920 131.33579           2            2       3.000000
2083      1 20.97684 1.710730 131.40853           2            2       3.000000
2084      1 21.98294 1.748584 133.74294           2            2       3.000000
2085      1 22.52404 1.752206 133.68935           2            2       3.000000
2086      1 24.36194 1.739450 133.34664           2            2       3.000000
2087      1 23.66471 1.738836 133.47264           2            2       3.000000
     nb_meal_day food_btw_meals smoke     ch2o calorie_check physical_act
1       3.000000              1     1 2.000000             0     0.000000
2       3.000000              1     2 3.000000             1     3.000000
3       3.000000              1     1 2.000000             0     2.000000
4       3.000000              1     1 2.000000             0     2.000000
5       1.000000              1     1 2.000000             0     0.000000
6       3.000000              1     1 2.000000             0     0.000000
7       3.000000              1     1 2.000000             0     1.000000
8       3.000000              1     1 2.000000             0     3.000000
9       3.000000              1     1 2.000000             0     1.000000
10      3.000000              1     1 2.000000             0     1.000000
11      3.000000              2     1 3.000000             0     2.000000
12      3.000000              2     1 2.000000             1     2.000000
13      3.000000              1     1 3.000000             0     2.000000
14      3.000000              1     1 2.000000             0     2.000000
15      1.000000              1     1 1.000000             0     1.000000
16      3.000000              3     1 2.000000             1     2.000000
17      1.000000              1     1 1.000000             0     1.000000
18      1.000000              1     1 2.000000             0     0.000000
19      4.000000              2     2 1.000000             0     0.000000
20      1.000000              1     1 2.000000             0     0.000000
21      3.000000              1     1 2.000000             0     3.000000
22      1.000000              1     2 2.000000             0     0.000000
23      3.000000              1     1 2.000000             0     1.000000
24      1.000000              1     1 2.000000             0     0.000000
25      3.000000              1     1 2.000000             0     0.000000
26      4.000000              2     2 2.000000             0     3.000000
27      1.000000              2     1 2.000000             0     1.000000
28      4.000000              2     1 2.000000             0     2.000000
29      3.000000              1     1 2.000000             1     3.000000
30      3.000000              1     1 2.000000             0     2.000000
31      4.000000              2     1 3.000000             0     0.000000
32      1.000000              1     1 1.000000             0     1.000000
33      3.000000              1     1 3.000000             0     1.000000
34      1.000000              1     1 2.000000             0     0.000000
35      4.000000              2     1 2.000000             0     2.000000
36      3.000000              1     1 2.000000             0     2.000000
37      3.000000              1     1 1.000000             1     2.000000
38      3.000000              1     1 1.000000             0     1.000000
39      3.000000              1     1 2.000000             0     1.000000
40      3.000000              1     1 3.000000             0     3.000000
41      3.000000              1     1 2.000000             0     1.000000
42      4.000000              2     1 2.000000             0     0.000000
43      3.000000              1     1 1.000000             0     0.000000
44      3.000000              1     2 2.000000             0     1.000000
45      3.000000             NA     1 2.000000             0     2.000000
46      3.000000              1     1 1.000000             0     0.000000
47      3.000000              2     1 3.000000             0     2.000000
48      3.000000              2     1 3.000000             0     2.000000
49      3.000000              1     1 2.000000             0     1.000000
50      3.000000              1     1 2.000000             0     0.000000
51      3.000000              1     1 3.000000             0     0.000000
52      3.000000              1     1 1.000000             0     0.000000
53      1.000000              1     1 2.000000             0     2.000000
54      3.000000             NA     1 2.000000             1     2.000000
55      4.000000              3     1 3.000000             0     2.000000
56      4.000000              2     1 2.000000             0     0.000000
57      3.000000              1     1 1.000000             0     0.000000
58      3.000000              1     1 1.000000             0     0.000000
59      1.000000              1     1 2.000000             0     1.000000
60      4.000000              1     1 3.000000             0     2.000000
61      3.000000              1     1 3.000000             0     3.000000
62      3.000000              1     1 2.000000             0     3.000000
63      1.000000             NA     1 2.000000             0     0.000000
64      3.000000              1     1 2.000000             0     0.000000
65      3.000000              2     1 1.000000             0     1.000000
66      3.000000              2     1 2.000000             0     0.000000
67      3.000000              3     1 2.000000             0     0.000000
68      3.000000              3     1 2.000000             0     0.000000
69      3.000000             NA     2 2.000000             1     0.000000
70      3.000000             NA     1 3.000000             0     0.000000
71      3.000000              3     1 2.000000             0     0.000000
72      3.000000             NA     1 3.000000             1     2.000000
73      3.000000              1     1 3.000000             1     1.000000
74      3.000000              1     1 3.000000             0     2.000000
75      3.000000              2     1 3.000000             0     0.000000
76      3.000000              1     1 3.000000             1     2.000000
77      3.000000             NA     1 2.000000             0     1.000000
78      3.000000              1     1 3.000000             0     0.000000
79      3.000000              1     1 3.000000             0     0.000000
80      3.000000              1     1 2.000000             0     0.000000
81      3.000000              1     1 2.000000             1     0.000000
82      3.000000              2     1 2.000000             0     0.000000
83      3.000000              3     1 2.000000             0     0.000000
84      3.000000             NA     1 3.000000             1     2.000000
85      3.000000              2     1 2.000000             0     0.000000
86      3.000000              1     1 2.000000             0     0.000000
87      1.000000              2     1 3.000000             0     1.000000
88      3.000000              1     1 2.000000             0     0.000000
89      3.000000              2     1 2.000000             1     1.000000
90      3.000000              1     1 1.000000             0     2.000000
91      4.000000              3     1 1.000000             0     2.000000
92      3.000000              3     1 2.000000             1     0.000000
93      4.000000              2     1 3.000000             1     3.000000
94      3.000000              3     1 2.000000             0     1.000000
95      3.000000              1     1 2.000000             0     2.000000
96      1.000000              2     1 2.000000             0     2.000000
97      3.000000              1     1 2.000000             0     3.000000
98      1.000000              2     1 1.000000             0     0.000000
99      3.000000              1     1 2.000000             0     3.000000
100     1.000000              1     1 1.000000             0     0.000000
101     3.000000              2     1 2.000000             0     0.000000
102     4.000000              2     1 2.000000             1     2.000000
103     4.000000              2     1 2.000000             0     1.000000
104     3.000000              1     1 2.000000             0     0.000000
105     1.000000              1     1 2.000000             0     2.000000
106     3.000000              3     1 1.000000             0     0.000000
107     3.000000              1     1 3.000000             0     3.000000
108     1.000000              1     1 3.000000             0     2.000000
109     3.000000              1     1 2.000000             0     0.000000
110     1.000000              1     1 1.000000             0     0.000000
111     4.000000              2     1 2.000000             0     1.000000
112     1.000000              1     1 2.000000             0     3.000000
113     1.000000              2     1 2.000000             0     1.000000
114     3.000000              2     1 2.000000             0     0.000000
115     3.000000              1     1 1.000000             0     3.000000
116     3.000000              2     1 2.000000             0     1.000000
117     3.000000              1     1 2.000000             0     1.000000
118     3.000000              2     2 3.000000             0     2.000000
119     3.000000              2     2 2.000000             0     0.000000
120     3.000000              2     1 2.000000             0     0.000000
121     4.000000              2     1 1.000000             0     1.000000
122     1.000000              1     1 2.000000             0     1.000000
123     3.000000              1     1 1.000000             0     0.000000
124     3.000000              3     1 2.000000             0     0.000000
125     3.000000              1     1 2.000000             0     1.000000
126     1.000000              1     1 3.000000             1     1.000000
127     3.000000              2     1 1.000000             0     0.000000
128     3.000000              1     1 3.000000             0     1.000000
129     4.000000              2     1 2.000000             0     1.000000
130     3.000000              1     1 1.000000             0     1.000000
131     3.000000              2     2 3.000000             1     1.000000
132     3.000000              3     1 2.000000             0     1.000000
133     3.000000              1     1 1.000000             0     2.000000
134     3.000000              1     1 2.000000             0     1.000000
135     3.000000              1     1 2.000000             0     1.000000
136     3.000000              1     2 3.000000             0     0.000000
137     3.000000              3     1 3.000000             1     3.000000
138     3.000000              2     1 2.000000             0     0.000000
139     3.000000              2     1 3.000000             0     1.000000
140     3.000000              3     1 2.000000             0     2.000000
141     3.000000              2     2 1.000000             0     1.000000
142     1.000000              1     1 1.000000             0     0.000000
143     4.000000              1     1 3.000000             1     2.000000
144     1.000000             NA     1 3.000000             0     1.000000
145     3.000000              2     1 2.000000             0     2.000000
146     3.000000              1     1 1.000000             0     0.000000
147     3.000000              3     1 2.000000             0     0.000000
148     1.000000              1     1 2.000000             0     2.000000
149     1.000000              1     1 2.000000             0     2.000000
150     3.000000              1     1 2.000000             0     2.000000
151     1.000000              3     2 2.000000             0     0.000000
152     3.000000              1     1 3.000000             0     2.000000
153     3.000000              1     1 2.000000             0     0.000000
154     1.000000              2     1 2.000000             0     1.000000
155     3.000000              2     1 1.000000             0     0.000000
156     3.000000              2     1 2.000000             0     2.000000
157     3.000000              1     1 3.000000             0     0.000000
158     1.000000              1     1 2.000000             0     0.000000
159     3.000000              1     2 1.000000             0     2.000000
160     3.000000              1     1 2.000000             0     1.000000
161     3.000000              3     2 2.000000             0     2.000000
162     3.000000              2     1 2.000000             1     3.000000
163     3.000000              2     1 3.000000             0     2.000000
164     3.000000              1     2 1.000000             0     1.000000
165     3.000000              3     1 2.000000             0     0.000000
166     3.000000              1     1 2.000000             0     1.000000
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561     3.566082              1     1 1.438398             0     2.000000
562     3.725797              1     1 1.191401             0     2.000000
563     3.520555              2     1 1.016968             0     0.651412
564     3.731212              2     1 1.876915             0     2.000000
565     4.000000              2     1 1.000000             0     2.000000
566     3.000000              2     1 1.000000             0     1.522399
567     1.259803              2     1 1.000000             0     1.374670
568     1.273128              2     1 1.695510             0     0.260079
569     3.000000              1     1 2.000000             0     2.784471
570     3.000000              1     1 2.000000             0     2.349495
571     3.304123              1     1 2.036764             0     2.038653
572     3.647154              2     1 1.266018             0     0.866045
573     3.000000              2     1 2.792074             0     0.630944
574     3.000000              2     1 1.426874             0     2.202080
575     3.300666              1     1 1.315608             0     0.069802
576     3.000000              1     1 1.963628             0     0.028202
577     3.535016              1     1 1.086391             0     0.618913
578     1.717608              1     1 1.179942             0     0.684739
579     3.000000              1     1 1.981260             0     2.306844
580     2.884479              1     1 1.439962             0     0.144627
581     4.000000              1     1 2.358172             0     2.000000
582     3.626815              1     1 2.000000             0     2.011519
583     4.000000              1     1 2.931438             0     2.000000
584     4.000000              1     1 2.000000             0     2.000000
585     4.000000              1     1 2.000000             0     2.000000
586     4.000000              1     1 2.000000             0     2.000000
587     3.000000              1     1 2.000000             0     0.000000
588     3.000000              1     1 2.000000             0     0.000000
589     3.000000              1     1 1.274815             0     0.520407
590     3.000000              2     1 1.000000             1     1.070331
591     3.000000              2     1 1.282460             1     1.601950
592     3.000000              2     1 2.875583             1     1.258504
593     1.000000              2     1 1.387531             0     0.000000
594     1.473088              2     1 1.164644             0     1.235675
595     1.000000              2     1 1.848703             0     0.000000
596     3.000000              1     1 1.325326             0     0.227985
597     3.166450              1     1 2.278578             0     1.838881
598     3.000000              1     1 1.153559             0     0.833976
599     3.494849              1     1 2.976672             0     1.949070
600     3.000000              2     1 2.975887             0     0.945093
601     2.993210              2     1 2.702783             0     1.967234
602     2.127797              2     1 2.120292             0     0.995735
603     3.000000              2     1 2.949419             1     1.295697
604     1.000000              2     1 1.000000             0     0.000000
605     1.000000              2     1 1.000000             0     0.000000
606     3.907790              2     1 1.109115             0     1.548953
607     3.699594              1     1 2.825629             0     1.699592
608     3.179995              2     1 1.910378             0     0.480614
609     1.075553              1     1 1.000000             0     0.127425
610     3.000000              1     1 1.965237             0     2.000000
611     3.000000              2     1 1.846626             0     2.460238
612     3.238258              2     1 1.014634             0     0.783676
613     3.804944              2     1 1.601140             0     0.174692
614     1.630846              1     1 2.975528             0     0.548991
615     3.000000              1     1 1.835545             0     1.162519
616     3.762778              1     1 2.000000             0     2.000000
617     3.000000              1     1 2.953192             0     2.830911
618     4.000000              1     1 2.285250             0     2.000000
619     3.371832              2     1 2.004126             0     2.000000
620     4.000000              1     1 1.000000             0     2.000000
621     4.000000              2     1 1.000000             0     1.683957
622     2.705445              2     1 1.333807             1     1.877369
623     3.341750              1     1 2.000000             0     2.511157
624     3.000000              2     1 2.685842             0     0.373186
625     3.000000              1     1 2.000000             0     0.000000
626     2.217651              1     1 2.013605             0     2.075293
627     4.000000              1     1 2.000000             0     2.000000
628     4.000000              1     1 2.000000             0     2.000000
629     3.000000              1     1 2.000000             0     0.027433
630     2.893778              2     1 1.000000             1     0.769726
631     1.000000              2     1 1.954889             0     0.000000
632     3.000000              1     1 1.751723             0     0.201136
633     3.502604              1     1 2.891713             0     1.696294
634     4.000000              1     1 2.369627             0     2.000000
635     3.998766              2     1 2.263466             0     2.000000
636     3.000000              2     1 2.044694             0     2.008256
637     3.193671              2     1 2.482933             0     2.000000
638     3.000000              2     1 1.041110             0     0.794402
639     1.696080              2     1 2.550307             0     1.098862
640     2.812377              2     1 2.346647             0     1.612248
641     1.612747              2     1 2.566629             0     1.190465
642     1.082304              2     1 1.220365             0     0.033328
643     1.882158              2     1 1.916812             0     0.417119
644     2.326233              2     1 2.306821             0     1.422370
645     3.000000              1     1 2.924594             1     1.352558
646     1.989398              1     1 2.241607             0     0.206025
647     1.735493              1     1 2.318736             0     1.193486
648     1.000000              2     1 1.131185             0     0.000000
649     2.974568              2     1 1.145761             1     0.879670
650     1.000000              2     1 1.198883             0     0.000000
651     1.000000              2     1 1.185062             0     0.000000
652     3.715118              2     1 1.774576             0     0.102970
653     3.489918              2     1 1.326694             0     0.791929
654     3.378859              1     1 1.000000             0     1.853425
655     3.263201              1     1 2.233274             0     1.557737
656     3.994588              1     1 2.548527             0     1.285976
657     3.249340              1     1 2.387945             0     1.976341
658     3.000000              1     1 2.000000             0     0.854337
659     3.087544              2     1 1.248180             0     1.952427
660     3.000000              1     1 2.000000             0     0.533309
661     1.163666              1     1 1.304910             0     0.252890
662     1.000000              1     1 1.676975             0     0.000000
663     1.000000              1     1 1.310074             0     0.000000
664     3.409363              1     1 1.543021             0     2.000000
665     3.000000              1     1 2.000000             0     1.862235
666     3.281391              1     1 1.960131             0     1.513029
667     3.000000              2     1 2.453384             0     1.321624
668     3.985250              1     1 2.654078             0     2.113151
669     3.000000              2     1 1.990788             0     0.486006
670     3.207071              1     1 1.029703             0     2.000000
671     3.471536              1     1 1.000000             0     2.000000
672     3.488342              1     1 1.670620             0     2.000000
673     3.443456              2     1 2.020764             0     1.078719
674     3.037790              2     1 2.212296             0     1.616882
675     3.642802              2     1 1.995177             0     0.118271
676     2.645858              1     1 2.357520             0     0.291309
677     3.420618              1     1 1.356405             0     1.351996
678     2.641550              1     1 2.121251             0     0.998391
679     3.000000              1     1 1.777486             0     1.077469
680     3.000000              1     1 1.873004             0     1.217180
681     3.000000              1     1 2.000000             0     0.680464
682     3.887906              1     1 2.000000             0     2.000000
683     3.435905              1     1 2.076933             0     2.026668
684     3.747163              1     1 2.575535             0     2.000000
685     2.625475              1     1 2.839558             1     1.949667
686     3.098399              1     1 2.196405             0     2.328147
687     3.125440              1     1 2.204263             0     2.407906
688     3.969810              1     1 2.000000             0     2.000088
689     3.712183              1     1 2.000000             0     2.002997
690     4.000000              1     1 2.542415             0     2.000000
691     3.000000              2     1 2.003570             0     1.259550
692     3.832911              1     1 2.993448             0     2.000000
693     3.000000              2     1 2.009796             0     2.000000
694     3.576103              1     1 1.949308             0     1.940182
695     3.565440              1     1 1.491268             0     1.951027
696     3.266644              1     1 1.095417             0     2.000000
697     3.433908              1     1 1.055019             0     0.819269
698     3.531038              1     1 1.134658             0     2.000000
699     3.998618              2     1 1.000000             0     2.000000
700     3.000000              2     1 1.000000             1     1.461005
701     1.226342              2     1 1.000000             0     0.144950
702     1.060796              2     1 1.596212             0     0.108948
703     3.000000              1     1 2.000000             0     2.697949
704     3.595761              1     1 2.000000             0     2.077653
705     3.737914              1     1 2.026547             0     2.009397
706     3.697831              2     1 1.147121             0     0.993058
707     3.210430              2     1 2.765876             0     0.611037
708     3.000000              2     1 1.064378             0     2.206055
709     3.452590              1     1 1.099231             0     0.418875
710     3.000000              1     1 1.994139             0     0.107981
711     3.205587              1     1 1.745959             0     0.115974
712     1.513835              1     1 1.549974             0     0.227802
713     3.000000              1     1 1.997744             0     2.432443
714     2.779379              2     1 1.249074             0     0.043412
715     4.000000              1     1 2.582165             0     2.000000
716     3.732126              1     1 2.000000             0     2.000934
717     3.937099              1     1 2.311791             0     2.013377
718     4.000000              1     1 2.000000             0     2.000000
719     3.047959              1     1 2.000000             0     2.011646
720     4.000000              1     1 2.000000             0     2.000000
721     3.000000              1     1 1.546856             0     0.512094
722     3.000000              1     1 1.755497             0     0.062932
723     3.000000              1     1 1.959531             0     0.520407
724     3.000000              2     1 1.015249             1     1.327833
725     2.975362              2     1 1.246822             1     0.979701
726     3.000000              2     1 2.487919             1     1.230441
727     1.000000              2     1 1.496810             0     0.000000
728     1.394539              2     1 1.322048             0     0.463949
729     1.000000              2     1 1.764055             0     0.000000
730     3.000000              1     1 1.927976             0     0.023574
731     3.131032              1     1 2.072194             0     1.487987
732     3.000000              1     1 1.283738             0     0.684879
733     1.578521              1     1 2.000000             0     2.000000
734     1.000000             NA     1 2.115967             0     0.770536
735     3.985442              1     1 2.147746             0     0.046836
736     3.623364              1     1 1.815293             0     0.989316
737     3.000000             NA     1 2.406541             0     0.949840
738     3.000000              1     1 1.015677             0     0.000000
739     3.363130              1     1 2.722063             0     3.000000
740     3.000000              1     1 1.345298             0     1.097905
741     3.000000              1     1 1.825629             0     0.699592
742     1.737620              1     1 2.207978             0     2.641072
743     3.000000              1     1 2.000000             0     0.345684
744     1.146052              1     1 1.074048             0     0.679935
745     3.981997              1     1 2.174248             0     0.920476
746     1.915400             NA     1 3.000000             0     1.000000
747     2.105616              1     1 1.456581             0     0.000000
748     3.000000              1     1 2.951056             0     0.000000
749     1.000000              1     1 2.000000             0     2.287423
750     1.000000             NA     1 2.803311             0     0.245354
751     3.000000              1     1 1.972074             0     0.228307
752     1.000000              1     1 2.000000             0     2.572230
753     3.714833              1     1 2.430918             0     2.545931
754     3.000000              1     1 2.229171             0     1.607953
755     3.000000              1     1 2.843777             0     2.022315
756     3.691226             NA     1 3.000000             0     1.228136
757     3.000000              2     1 2.000000             0     0.000000
758     3.292386              1     1 3.000000             0     2.000000
759     3.000000              1     1 2.746197             0     1.910432
760     1.104642              1     1 1.530046             0     1.360635
761     3.000000              1     1 2.000000             0     2.707882
762     3.000000              1     1 1.636326             0     1.000000
763     3.000000              1     1 1.701835             0     1.682271
764     3.559841              1     1 2.367996             0     0.235530
765     3.000000              1     1 3.000000             0     1.096818
766     3.000000              1     1 2.292073             0     1.067817
767     3.000000              1     1 1.549931             0     0.000000
768     3.000000              1     1 1.000000             0     0.000000
769     3.654061              1     1 1.229915             0     0.793489
770     1.296156              1     1 2.000000             1     0.110174
771     1.000000              1     1 2.000000             1     0.694281
772     2.656588              1     1 2.061062             0     1.912981
773     2.809716              1     1 1.591425             0     0.178023
774     2.776840              1     1 1.278231             0     0.557130
775     3.000000              1     1 2.000000             0     2.892922
776     1.000000              1     1 2.628985             0     1.000000
777     1.000000              1     1 2.000000             1     0.926592
778     1.999014              1     1 2.326165             0     0.007050
779     2.644692              1     1 1.226764             1     0.747528
780     2.449723              1     1 2.000000             1     0.479592
781     3.000000              1     1 1.174611             0     2.000000
782     3.000000              1     1 1.000000             0     0.000000
783     2.794156              1     1 2.377362             0     0.101236
784     4.000000              1     1 2.426465             0     2.000000
785     1.146794              1     1 3.000000             0     1.809745
786     1.131695              1     1 1.344539             0     0.000000
787     3.000000              1     1 2.425927             0     1.000000
788     3.000000              1     1 1.817983             1     0.000000
789     3.000000              1     1 1.205633             0     2.000000
790     2.488189              1     1 2.000000             0     2.000000
791     1.000000             NA     1 2.008760             0     0.631190
792     1.000000             NA     1 2.069184             0     0.729898
793     3.999591              1     1 2.495944             0     0.003420
794     3.000000              1     1 1.957871             0     0.994592
795     3.612941             NA     1 2.043703             0     0.562118
796     3.950553             NA     1 2.109858             0     0.508847
797     3.000000              1     1 2.177386             0     0.336814
798     3.000000              1     1 2.168651             0     0.977998
799     2.951837              1     1 2.112032             0     0.378683
800     2.799979              1     1 1.081333             0     0.444347
801     3.000000              1     1 1.005727             0     0.000000
802     3.000000              1     1 2.000000             0     0.129163
803     3.000000              1     1 2.000000             0     0.000000
804     2.228113              1     1 2.045004             0     1.293665
805     3.042774              1     1 1.277636             0     0.944982
806     1.198643              1     1 1.000000             0     0.000000
807     1.555557              1     1 1.000000             0     0.000000
808     3.987707              1     1 2.597746             0     1.922234
809     3.995957              1     1 2.632871             0     1.236114
810     1.047197              1     1 2.000000             0     0.151360
811     3.000000              1     1 2.429911             0     0.119640
812     3.000000              1     1 2.972426             0     2.160790
813     1.000000              1     1 2.000000             0     2.063943
814     1.000000              1     1 2.000000             0     2.119682
815     3.000000              1     1 1.926577             0     1.581242
816     3.000000              1     1 1.771781             0     0.759422
817     1.000000              1     1 2.000000             0     2.315983
818     1.193589              1     1 2.000000             0     2.000000
819     3.000000              1     1 2.759246             0     1.763277
820     3.000000              1     1 2.734782             0     2.207881
821     3.000000              1     1 2.721356             0     1.528968
822     3.390143              1     1 2.133767             0     2.318492
823     3.546352              1     1 2.081121             0     1.993666
824     3.266501             NA     1 2.926995             0     0.985872
825     3.554974             NA     1 2.217957             0     0.978120
826     2.372311              1     1 1.810310             0     0.094893
827     2.106010              1     1 1.639202             0     1.117311
828     4.000000              1     1 2.374044             0     2.000000
829     3.715306              1     1 2.876096             0     1.815768
830     3.376717              1     1 2.184843             0     1.358185
831     1.000000              1     1 2.000000             0     2.971832
832     1.000000              1     1 2.000000             0     2.877473
833     1.211606              1     1 2.568063             0     3.000000
834     3.000000              1     1 1.333707             0     1.000000
835     3.000000              1     1 1.164062             0     1.000000
836     3.000000              1     1 2.000000             0     1.722053
837     3.989550              1     1 2.028426             0     0.815170
838     3.171082              1     1 2.364498             0     1.224743
839     3.000000              1     1 2.824559             0     0.754599
840     3.000000              1     1 3.000000             0     2.433918
841     3.000000              1     1 1.077917             0     0.523847
842     3.000000              1     1 1.000000             0     0.000000
843     3.000000              1     1 1.131448             0     0.061366
844     3.105007              1     1 1.172427             0     0.184917
845     3.000000              1     1 1.040342             1     0.497373
846     3.000000              1     1 1.274718             1     0.129902
847     1.000000              1     1 2.000000             1     0.736032
848     1.590982              1     1 1.977801             0     1.264616
849     1.704828              1     1 1.000000             0     0.792929
850     2.725012              1     1 1.622440             0     0.902661
851     2.372339              1     1 1.675733             0     1.066241
852     1.097312              1     1 1.328835             0     1.264257
853     3.000000              1     1 2.756916             0     1.016042
854     1.000000              1     1 2.000000             0     0.977929
855     1.068196              1     1 1.364957             1     0.000000
856     1.000000              1     1 2.000000             1     0.371452
857     1.411685              1     1 1.859089             0     0.000000
858     2.752318              1     1 2.328331             0     0.815509
859     3.000000              1     1 1.696691             1     0.942240
860     2.737571              1     1 1.144467             1     0.753782
861     2.973504              1     1 2.000000             1     0.178976
862     2.608055              1     1 1.859160             0     2.000000
863     2.625942              1     1 1.045586             0     0.553305
864     1.411808              1     1 2.651194             0     2.878414
865     1.095223              1     1 2.474132             0     2.876696
866     3.308460              1     1 2.078011             0     1.779646
867     3.821461              1     1 2.397124             0     2.044165
868     3.000000              1     1 2.813234             0     0.228753
869     2.658639              1     1 2.781628             0     1.955992
870     1.000000              1     1 2.203120             0     0.104451
871     3.000000              1     1 1.871033             0     1.000000
872     2.339614              1     1 1.711074             1     0.095517
873     1.713762              1     1 1.797161             0     1.658698
874     2.743277              1     1 1.394883             0     2.000000
875     1.000000             NA     1 2.881677             0     0.915699
876     1.000000             NA     1 2.795651             0     0.808730
877     3.051804              1     1 2.111913             0     0.843709
878     3.245148              1     1 1.971774             0     0.989335
879     3.563744              1     1 1.853991             0     0.997731
880     3.000000             NA     1 2.197732             0     0.827506
881     3.829101              1     1 1.641022             0     1.554817
882     3.058539              1     1 1.130079             0     2.834373
883     3.196043              1     1 1.352649             0     0.148628
884     3.000000              1     1 1.968011             0     1.072662
885     3.000000              1     1 2.100112             0     1.171160
886     3.000000              1     1 1.362642             0     0.921268
887     1.391778              1     1 2.645480             0     2.685087
888     3.000000              1     1 2.000000             0     0.121585
889     3.000000              1     1 2.000000             0     0.334579
890     1.137150              1     1 1.000695             0     0.261274
891     3.322522              1     1 2.616285             0     0.943058
892     3.269088              1     1 2.039535             0     0.779686
893     2.938135             NA     1 2.943097             0     0.997202
894     1.259628             NA     1 3.000000             0     1.000000
895     2.965494              1     1 2.868132             0     0.000000
896     3.000000              1     1 2.526775             0     0.271174
897     2.845307              1     1 2.638164             0     0.000000
898     1.000000              1     1 2.000000             0     1.061743
899     1.000000              1     1 2.000000             0     2.269058
900     1.000000             NA     1 2.976177             0     0.780117
901     3.000000              1     1 1.827515             0     0.480206
902     3.000000              1     1 1.305816             0     0.170452
903     1.000000              1     1 2.663861             0     2.762711
904     3.095663              1     1 2.568157             0     2.724300
905     3.483449              1     1 2.359246             0     2.060415
906     3.000000              1     1 2.763573             0     1.502711
907     3.156309             NA     1 3.000000             0     1.179592
908     3.728377             NA     1 2.682107             0     1.123931
909     3.000000              1     1 2.526193             0     0.925118
910     3.000000              1     1 2.000000             0     0.092344
911     3.608850              1     1 2.878866             0     2.000000
912     3.370362              1     1 2.558773             0     1.206121
913     3.000000              1     1 2.046766             0     2.172546
914     2.390070              1     1 1.648404             0     0.874643
915     1.101404              1     1 1.579207             0     1.153775
916     2.756405              1     1 2.079131             0     2.931527
917     3.000000              1     1 1.081597             0     1.000000
918     3.000000              1     1 1.833055             0     1.000000
919     3.000000              1     1 1.281683             0     1.609801
920     3.054899              1     1 2.080968             0     1.107543
921     3.118013              1     1 2.136159             0     0.783963
922     3.000000              1     1 2.152666             0     1.078074
923     3.335876              1     1 2.372444             0     1.305633
924     3.000000              1     1 1.706568             0     0.784216
925     3.000000              1     1 1.000000             0     0.000000
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927     3.648194              1     1 1.279756             0     0.132629
928     1.865238              1     1 2.000000             1     0.320209
929     2.118153              1     1 1.075239             1     0.085388
930     1.000000              1     1 1.438018             1     0.110887
931     2.961113              1     1 2.657303             0     1.911080
932     2.400943              1     1 1.509734             0     0.167125
933     2.870005              1     1 1.150310             0     0.417724
934     3.000000              1     1 2.396977             0     0.577063
935     1.000000             NA     1 2.813530             0     1.000000
936     1.000000              1     1 2.000000             1     0.948930
937     1.000000              1     1 1.334856             1     0.732276
938     1.030416              1     1 2.118716             0     0.529259
939     2.696051              1     1 1.622827             1     0.539952
940     2.762883              1     1 1.512035             1     0.303722
941     2.401341              1     1 2.000000             1     0.663896
942     2.298612              1     1 2.000000             1     0.547515
943     3.618722              1     1 1.274389             0     1.504003
944     2.562895              1     1 1.017006             0     0.588673
945     2.849848              1     1 1.028538             0     0.675983
946     2.805436              1     1 2.122884             0     0.045651
947     3.715148              1     1 2.703337             0     2.000000
948     3.788602              1     1 2.429059             0     2.094542
949     1.893811              1     1 3.000000             0     1.376229
950     1.010319              1     1 1.319437             0     0.000000
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953     2.669766              1     1 1.979848             1     0.445238
954     1.322087              1     1 2.000000             0     0.540397
955     2.041558              1     1 2.387250             0     0.813917
956     3.000000              1     1 2.000000             0     1.666390
957     2.468421              1     1 2.000000             0     2.000000
958     2.110937              1     1 2.175632             0     1.000000
959     3.071028              1     1 1.738959             0     0.090147
960     3.292956              1     1 1.723372             0     0.000000
961     3.000000              1     1 2.000000             0     2.936551
962     3.000000              1     1 2.000000             0     0.451078
963     2.283673              1     1 1.970622             0     1.863258
964     1.124977              1     1 2.763820             0     0.855400
965     2.994800              1     1 1.290979             0     0.875990
966     3.000000              1     1 2.000000             0     2.177243
967     2.983297              1     1 1.453626             0     0.328960
968     3.000000              1     1 2.957821             0     2.242754
969     3.000000              1     1 1.937674             0     1.417035
970     3.000000              1     1 1.131169             0     0.095389
971     1.890213              1     1 2.000000             0     0.000000
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974     2.256119              1     1 1.142873             0     0.807076
975     3.000000              1     1 2.020424             0     0.000000
976     2.664800              1     1 1.810738             0     0.809890
977     3.000000              1     1 3.000000             0     0.551810
978     3.000000              1     1 1.184230             0     0.155579
979     3.000000              1     1 2.879402             0     0.000000
980     3.000000              1     1 1.699971             0     1.727114
981     2.547086              1     1 1.538626             0     1.000000
982     2.812283              1     1 3.000000             0     0.362441
983     2.278652              1     1 2.000000             0     1.806740
984     1.240046              1     1 2.000000             0     0.000000
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987     3.000000              1     1 2.269564             0     0.503122
988     1.660768              1     1 2.482294             0     0.891205
989     2.597608              1     1 2.000000             0     0.000000
990     3.000000              1     1 2.000000             0     1.000000
991     3.000000              1     1 2.653831             0     1.965820
992     3.000000              1     1 2.000000             0     0.826609
993     2.044035              1     1 1.954968             0     1.000000
994     3.000000              1     1 1.000000             0     0.000000
995     2.038373              1     1 2.000000             0     2.708250
996     1.000000              1     1 1.000000             0     0.262171
997     1.000000              1     1 1.000000             0     0.740633
998     1.474836              1     1 2.000000             0     0.274838
999     3.986652              1     1 2.000000             0     0.934286
1000    2.720642              1     1 1.519700             0     0.279375
1001    1.976744              1     1 1.628997             0     0.880584
1002    3.000000              1     1 2.981604             0     0.000000
1003    3.000000              1     1 1.517225             0     0.000000
1004    2.679724              1     1 1.999737             0     1.747347
1005    1.619796              1     1 1.642251             0     0.662831
1006    2.592570              1     1 2.549617             0     1.427233
1007    1.546665              1     1 3.000000             0     1.000000
1008    3.339914              1     1 2.000000             0     1.000000
1009    3.884861              1     1 2.000000             0     1.000000
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1012    3.000000              1     1 2.802498             0     1.000000
1013    3.000000              1     1 2.877810             0     1.000000
1014    1.000000              1     1 1.708083             0     1.876051
1015    1.802305              1     1 2.000000             0     0.390877
1016    3.000000              1     1 3.000000             0     1.072318
1017    2.797600              1     1 2.991671             0     2.148738
1018    2.627173              1     1 2.253422             0     2.690756
1019    1.000000              1     1 2.000000             0     1.472172
1020    1.032887              1     1 2.397653             0     2.358699
1021    3.014808              1     1 2.000000             0     1.997529
1022    2.271734              1     1 2.629043             0     1.000000
1023    2.122545              1     1 2.984153             0     2.164472
1024    3.000000              1     1 2.000000             0     1.732340
1025    3.000000              1     1 2.000000             0     1.655488
1026    2.902766              1     1 1.753464             0     1.729987
1027    1.000000              1     1 1.056680             0     1.516147
1028    2.687502              1     1 2.000000             0     2.217220
1029    3.000000              1     1 2.205911             0     1.293396
1030    3.000000              1     1 2.000000             0     1.271667
1031    1.178708              1     1 1.791286             0     0.893362
1032    3.000000              1     1 1.117464             0     1.000000
1033    2.279546              1     1 2.000000             0     0.000000
1034    2.677693              1     1 2.693978             0     0.000000
1035    3.530090              1     1 1.172186             0     0.678943
1036    3.000000              1     1 2.641642             0     0.285889
1037    1.835543              1     1 1.718569             0     0.674348
1038    2.853676              1     1 1.526313             0     1.000000
1039    2.337035              1     1 1.830614             0     0.706287
1040    3.000000              1     1 1.000000             0     0.000000
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1043    1.005391              1     1 2.001936             0     1.000000
1044    3.250467              1     1 1.367876             0     0.022958
1045    3.000000              1     1 2.000000             0     2.939733
1046    2.948721              1     1 1.984323             0     0.986414
1047    1.809930              1     1 2.174835             0     0.340915
1048    3.000000              1     1 1.849997             0     0.954459
1049    3.000000              1     1 2.825629             0     3.000000
1050    3.000000              1     1 1.207978             0     1.916751
1051    1.946710              1     1 2.000000             0     0.000000
1052    2.979600              1     1 1.964435             0     0.189957
1053    2.994198              1     1 2.747302             0     0.000000
1054    3.000000              1     1 2.137550             0     0.000000
1055    1.894384              1     1 2.000000             0     0.000000
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1057    3.000000              1     1 1.507638             0     1.824340
1058    2.579291              1     1 1.909253             0     1.000000
1059    3.000000              1     1 2.472964             0     1.856119
1060    2.449067              1     1 2.000000             0     0.926350
1061    3.000000              1     1 1.002292             0     0.000000
1062    3.000000              1     1 2.147074             0     1.416076
1063    3.000000              1     1 2.803002             0     1.984480
1064    1.532833              1     1 1.931242             0     0.525002
1065    3.000000              1     1 2.000000             0     1.000000
1066    3.000000              1     1 3.000000             0     2.000000
1067    2.961706              1     1 2.000000             0     1.661556
1068    3.000000              1     1 1.029798             0     0.000000
1069    1.152521              1     1 1.723159             0     1.390160
1070    1.104642              1     1 1.000000             0     0.000000
1071    1.000000              1     1 1.639807             0     0.072117
1072    1.729553              1     1 1.400247             0     0.887923
1073    3.000000              1     1 2.994515             0     0.000000
1074    1.346987              1     1 1.868349             0     0.702538
1075    1.977221              1     1 3.000000             0     1.000000
1076    2.443812              1     1 2.707927             0     0.702839
1077    2.491315              1     1 2.450069             0     0.000000
1078    3.000000              1     1 1.568035             0     0.870127
1079    2.843319              1     1 2.832004             0     1.000000
1080    2.157164              1     1 1.087166             0     0.110174
1081    3.000000              1     1 2.503198             0     0.000000
1082    2.601675              1     1 2.469469             0     1.736538
1083    1.171027              1     1 2.405172             0     2.300282
1084    2.776840              1     1 1.651548             0     0.000000
1085    3.362758              1     1 2.000000             0     2.892922
1086    1.672706              1     1 2.766674             0     1.000000
1087    2.714115              1     1 2.279214             0     0.970661
1088    2.521546              1     1 1.985312             0     0.007050
1089    1.338033              1     1 1.944095             0     1.931829
1090    3.000000              1     1 2.000000             0     2.040816
1091    3.000000              1     1 2.035954             0     1.210736
1092    3.000000              1     1 2.452986             0     1.000000
1093    3.000000              1     1 1.622638             0     0.033745
1094    1.081805              1     1 2.000000             0     0.000000
1095    3.087119              1     1 2.000000             0     1.403872
1096    2.909117              1     1 1.221281             0     0.503279
1097    3.000000              1     1 2.413813             0     0.458237
1098    1.466393              1     1 2.442775             0     0.675262
1099    1.471053              1     1 2.000000             0     0.467562
1100    2.983201              1     1 2.168904             0     0.752926
1101    3.000000              1     1 2.000000             0     2.847761
1102    2.783336              1     1 2.000000             0     2.536615
1103    1.863012              1     1 2.196471             0     1.000000
1104    3.053598              1     1 1.919629             0     0.408023
1105    3.165837              1     1 1.844645             0     0.889963
1106    3.000000              1     1 2.000000             0     2.270555
1107    3.000000              1     1 2.000000             0     0.146919
1108    2.741413              1     1 1.996384             0     1.276552
1109    1.058123              1     1 2.265727             0     0.885633
1110    2.997414              1     1 1.259454             0     0.935217
1111    1.672958              1     1 2.000000             0     1.582428
1112    1.680838              1     1 1.833635             0     0.281876
1113    3.000000              1     1 2.999495             0     2.405963
1114    3.070386              1     1 1.962322             0     1.145752
1115    3.000000              1     1 1.163111             0     1.926381
1116    2.608416              1     1 1.204175             0     0.000000
1117    1.599464              1     1 2.000000             0     0.170480
1118    2.815255              1     1 1.622214             0     0.463891
1119    2.395785              1     1 1.400770             0     0.788659
1120    3.000000              1     1 2.056053             0     0.000000
1121    2.844138              1     1 1.983649             0     0.868721
1122    3.000000              1     1 3.000000             0     0.897702
1123    3.000000              1     1 1.199517             0     0.047738
1124    3.000000              1     1 2.386904             0     0.926201
1125    3.000000              1     1 2.165605             0     0.855973
1126    2.716106              1     1 1.660614             0     1.000000
1127    1.402771              1     1 3.000000             0     0.112383
1128    2.865657              1     1 2.000000             0     1.287750
1129    1.836226              1     1 1.855370             0     0.000000
1130    2.884848              1     1 2.154898             0     0.325534
1131    2.375026              1     1 1.398540             0     0.000000
1132    2.977400              1     1 1.522426             0     0.501417
1133    2.693646              1     1 2.606690             0     1.170823
1134    2.832018              1     1 2.456939             0     0.038809
1135    3.000000              1     1 2.000000             0     1.000000
1136    3.000000              1     1 2.978465             0     1.974656
1137    3.000000              1     1 2.000000             0     0.527341
1138    2.270163              1     1 1.992586             0     1.452467
1139    3.000000              1     1 1.302594             0     0.000000
1140    2.646717              1     1 2.000000             0     2.627515
1141    1.000000              1     1 1.278578             0     0.021120
1142    1.193729              1     1 1.846441             0     0.546402
1143    1.477581              1     1 1.995035             0     0.324676
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1145    2.756622              1     1 1.606076             0     0.432973
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1147    3.000000              1     1 2.166024             0     0.000000
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1150    1.818026              1     1 2.088929             0     0.305422
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1152    1.724887              1     1 3.000000             0     1.000000
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1154    3.856434              1     1 2.000000             0     1.324170
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1156    3.000000              1     1 1.146595             0     0.313810
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1160    2.049908              1     1 1.999882             0     1.637368
1161    3.000000              1     1 3.000000             0     1.047290
1162    1.044628              1     1 2.010510             0     2.805801
1163    2.982120              1     1 2.778587             0     2.667739
1164    1.009426              1     1 2.136398             0     1.060349
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1166    3.000974              1     1 2.000000             0     2.274248
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1168    2.598079              1     1 2.905450             0     1.600431
1169    3.000000              1     1 2.000000             0     1.194519
1170    2.837388              1     1 2.159987             0     1.475772
1171    2.946063              1     1 1.307563             0     1.442485
1172    1.000000              1     1 1.336378             0     1.898889
1173    1.873484              1     1 2.000000             0     2.079709
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1177    2.623079              1     1 2.535127             0     1.030752
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1916    3.000000              1     1 2.688229             0     0.000000
1917    3.000000              1     1 2.695094             0     0.000000
1918    3.000000              1     1 2.618198             0     0.000000
1919    3.000000              1     1 2.555189             0     0.000000
1920    3.000000              1     1 1.419136             0     1.700889
1921    3.000000              1     1 1.422483             0     1.708971
1922    3.000000              1     1 2.426094             0     0.294763
1923    3.000000              1     1 2.495961             0     0.189831
1924    3.000000              1     1 1.797041             0     1.427413
1925    3.000000              1     1 1.768111             0     0.616503
1926    3.000000              1     1 2.365188             0     1.256115
1927    3.000000              1     1 2.299156             0     1.699056
1928    3.000000              1     1 2.684819             0     1.465250
1929    3.000000              1     1 2.841740             0     1.429256
1930    3.000000              1     1 2.796894             0     0.382189
1931    3.000000              1     1 2.987718             0     0.387074
1932    3.000000              1     1 2.675567             0     0.000000
1933    3.000000              1     1 2.770125             0     0.000000
1934    3.000000              1     1 2.621877             0     0.000000
1935    3.000000              1     1 2.720050             0     0.000000
1936    3.000000              1     1 2.436097             0     0.129178
1937    3.000000              1     1 2.535629             0     0.152713
1938    3.000000              1     1 2.678779             0     0.000000
1939    3.000000              1     1 2.569713             0     0.000000
1940    3.000000              1     1 1.759352             0     1.469928
1941    3.000000              1     1 1.837184             0     1.525165
1942    3.000000              1     1 2.856795             0     1.046463
1943    3.000000              1     1 2.846259             0     1.295759
1944    3.000000              1     1 1.067716             0     0.030541
1945    3.000000              1     1 1.029241             0     0.075776
1946    3.000000              1     1 1.074412             0     0.006265
1947    3.000000              1     1 1.030501             0     0.065820
1948    3.000000              1     1 2.699675             0     1.520818
1949    3.000000              1     1 2.830247             0     1.783138
1950    3.000000              1     1 2.305521             0     1.956278
1951    3.000000              1     1 2.155926             0     1.999836
1952    3.000000              1     1 2.784303             0     0.000000
1953    3.000000              1     1 2.786417             0     0.000000
1954    3.000000              1     1 2.707201             0     0.000000
1955    3.000000              1     1 2.709428             0     0.000000
1956    3.000000              1     1 1.525127             0     0.220825
1957    3.000000              1     1 1.554925             0     0.209586
1958    3.000000              1     1 1.170515             0     0.264831
1959    3.000000              1     1 1.436616             0     0.167086
1960    3.000000              1     1 1.242832             0     0.530925
1961    3.000000              1     1 1.542490             0     0.627700
1962    3.000000              1     1 1.205548             0     1.723934
1963    3.000000              1     1 1.301657             0     1.718543
1964    3.000000              1     1 2.704315             0     0.000000
1965    3.000000              1     1 2.639816             0     0.000000
1966    3.000000              1     1 2.708927             0     0.000000
1967    3.000000              1     1 2.704827             0     0.000000
1968    3.000000              1     1 1.395400             0     1.931173
1969    3.000000              1     1 2.611654             0     1.618512
1970    3.000000              1     1 2.868167             0     1.483720
1971    3.000000              1     1 2.247979             0     1.609938
1972    3.000000              1     1 2.702789             0     1.606109
1973    3.000000              1     1 1.663213             0     1.094035
1974    3.000000              1     1 2.414739             0     1.917014
1975    3.000000              1     1 2.351193             0     1.051889
1976    3.000000              1     1 2.896088             0     1.612741
1977    3.000000              1     1 2.862408             0     1.456933
1978    3.000000              1     1 2.887659             0     1.480919
1979    3.000000              1     1 2.893062             0     1.408177
1980    3.000000              1     1 2.619517             0     0.000000
1981    3.000000              1     1 2.682804             0     0.000000
1982    3.000000              1     1 2.552388             0     0.000000
1983    3.000000              1     1 2.617401             0     0.000000
1984    3.000000              1     1 2.654636             0     0.069238
1985    3.000000              1     1 2.569364             0     0.159255
1986    3.000000              1     1 1.322004             0     0.000000
1987    3.000000              1     1 1.292479             0     0.000000
1988    3.000000              1     1 2.839069             0     1.231031
1989    3.000000              1     1 2.843782             0     1.302507
1990    3.000000              1     1 2.858389             0     1.444382
1991    3.000000              1     1 2.864933             0     1.501647
1992    3.000000              1     1 1.031701             0     0.034650
1993    3.000000              1     1 1.000536             0     0.005939
1994    3.000000              1     1 1.020313             0     0.108386
1995    3.000000              1     1 1.063422             0     0.051097
1996    3.000000              1     1 2.451260             0     0.043689
1997    3.000000              1     1 2.613504             0     0.067985
1998    3.000000              1     1 1.042989             0     0.097114
1999    3.000000              1     1 1.237557             0     0.083675
2000    3.000000              1     1 2.833770             0     0.806422
2001    3.000000              1     1 2.816052             0     1.571865
2002    3.000000              1     1 2.682909             0     1.318743
2003    3.000000              1     1 2.640539             0     1.879818
2004    3.000000              1     1 2.472903             0     1.998047
2005    3.000000              1     1 2.372058             0     1.324805
2006    3.000000              1     1 2.387991             0     0.850715
2007    3.000000              1     1 2.322003             0     0.886602
2008    3.000000              1     1 2.737353             0     0.000000
2009    3.000000              1     1 2.739351             0     0.000000
2010    3.000000              1     1 2.632498             0     0.000000
2011    3.000000              1     1 2.632253             0     0.000000
2012    3.000000              1     1 2.577210             0     0.000000
2013    3.000000              1     1 2.419153             0     0.019404
2014    3.000000              1     1 2.609052             0     0.000000
2015    3.000000              1     1 2.476002             0     0.000000
2016    3.000000              1     1 2.225139             0     0.035928
2017    3.000000              1     1 2.556068             0     0.016820
2018    3.000000              1     1 1.423073             0     0.197993
2019    3.000000              1     1 1.299194             0     0.234303
2020    3.000000              1     1 1.679489             0     0.017804
2021    3.000000              1     1 1.495830             0     0.109327
2022    3.000000              1     1 1.817860             0     0.011161
2023    3.000000              1     1 1.967707             0     0.014370
2024    3.000000              1     1 1.441289             0     1.425712
2025    3.000000              1     1 1.144539             0     0.922014
2026    3.000000              1     1 1.353167             0     1.093679
2027    3.000000              1     1 1.409444             0     0.933595
2028    3.000000              1     1 1.959287             0     1.433151
2029    3.000000              1     1 1.931163             0     1.562213
2030    3.000000              1     1 1.651462             0     1.655993
2031    3.000000              1     1 1.683448             0     1.645532
2032    3.000000              1     1 2.395387             0     0.000272
2033    3.000000              1     1 2.523793             0     0.000000
2034    3.000000              1     1 2.638896             0     0.000000
2035    3.000000              1     1 2.666178             0     0.000000
2036    3.000000              1     1 2.495851             0     0.000000
2037    3.000000              1     1 2.609188             0     0.000000
2038    3.000000              1     1 2.471721             0     0.000096
2039    3.000000              1     1 2.447306             0     0.000454
2040    3.000000              1     1 1.674061             0     1.683497
2041    3.000000              1     1 1.678791             0     1.682490
2042    3.000000              1     1 1.777805             0     1.584716
2043    3.000000              1     1 1.733306             0     1.773304
2044    3.000000              1     1 2.435978             0     0.232742
2045    3.000000              1     1 2.909675             0     0.360908
2046    3.000000              1     1 2.751370             0     0.324913
2047    3.000000              1     1 2.887909             0     0.312923
2048    3.000000              1     1 2.411582             0     0.985287
2049    3.000000              1     1 2.386390             0     1.544632
2050    3.000000              1     1 2.232601             0     0.554323
2051    3.000000              1     1 1.755907             0     1.488090
2052    3.000000              1     1 2.336349             0     1.416400
2053    3.000000              1     1 2.562002             0     1.976427
2054    3.000000              1     1 2.305574             0     1.734424
2055    3.000000              1     1 2.319912             0     1.582675
2056    3.000000              1     1 2.701960             0     1.465909
2057    3.000000              1     1 2.474518             0     1.560261
2058    3.000000              1     1 2.748569             0     1.427037
2059    3.000000              1     1 2.827773             0     1.412357
2060    3.000000              1     1 2.632224             0     0.300964
2061    3.000000              1     1 2.868679             0     0.115369
2062    3.000000              1     1 2.930137             0     0.043101
2063    3.000000              1     1 2.961899             0     0.259424
2064    3.000000              1     1 2.550672             0     0.000000
2065    3.000000              1     1 2.584305             0     0.000000
2066    3.000000              1     1 2.621976             0     0.000000
2067    3.000000              1     1 2.722276             0     0.000000
2068    3.000000              1     1 2.634342             0     0.000000
2069    3.000000              1     1 2.619390             0     0.000000
2070    3.000000              1     1 2.786780             0     0.000000
2071    3.000000              1     1 2.768141             0     0.000000
2072    3.000000              1     1 1.716590             0     0.001272
2073    3.000000              1     1 2.094901             0     0.070890
2074    3.000000              1     1 2.347322             0     0.008013
2075    3.000000              1     1 2.331123             0     0.063383
2076    3.000000              1     1 1.757105             0     0.085119
2077    3.000000              1     1 2.506631             0     0.025787
2078    3.000000              1     1 2.487070             0     0.067329
2079    3.000000              1     1 2.320068             0     0.045246
2080    3.000000              1     1 1.650612             0     1.537639
2081    3.000000              1     1 1.610768             0     1.510398
2082    3.000000              1     1 1.796267             0     1.728332
2083    3.000000              1     1 1.728139             0     1.676269
2084    3.000000              1     1 2.005130             0     1.341390
2085    3.000000              1     1 2.054193             0     1.414209
2086    3.000000              1     1 2.852339             0     1.139107
2087    3.000000              1     1 2.863513             0     1.026452
     use_tech freq_alcohol m_trans obesity_lev
1    1.000000           NA       4           2
2    0.000000            2       4           2
3    1.000000            3       4           2
4    0.000000            3       5           3
5    0.000000            2       4           4
6    0.000000            2       1           2
7    0.000000            2       3           2
8    0.000000            2       4           2
9    1.000000            3       4           2
10   1.000000           NA       4           2
11   2.000000            2       4           5
12   1.000000            2       4           4
13   0.000000            2       4           2
14   1.000000            3       1           5
15   1.000000            2       4           2
16   1.000000            2       4           2
17   0.000000            2       4           4
18   0.000000           NA       1           5
19   0.000000           NA       1           4
20   0.000000            2       4           3
21   2.000000           NA       5           4
22   0.000000           NA       1           5
23   0.000000            2       1           2
24   2.000000            2       4           5
25   1.000000            2       4           2
26   2.000000           NA       4           2
27   2.000000            4       5           2
28   1.000000            2       1           2
29   1.000000            2       4           2
30   2.000000            3       5           2
31   1.000000           NA       3           3
32   0.000000            2       4           4
33   1.000000            2       5           2
34   0.000000            2       4           4
35   0.000000            2       4           2
36   2.000000            2       4           4
37   0.000000           NA       5           2
38   0.000000            2       4           2
39   1.000000            2       4           2
40   0.000000            2       4           4
41   0.000000            2       4           3
42   0.000000           NA       5           2
43   0.000000           NA       4           2
44   1.000000            3       4           2
45   0.000000           NA       4           2
46   1.000000           NA       4           4
47   0.000000            2       4           2
48   0.000000            2       4           2
49   0.000000            2       5           2
50   0.000000            2       4           2
51   1.000000            2       5           2
52   0.000000            2       1           2
53   2.000000           NA       4           2
54   1.000000           NA       4           2
55   0.000000           NA       4           2
56   0.000000            2       1           2
57   1.000000            2       1           2
58   1.000000           NA       4           2
59   1.000000           NA       5           2
60   2.000000           NA       4           1
61   1.000000            2       4           2
62   1.000000            2       4           2
63   0.000000            2       4           2
64   2.000000            2       1           2
65   1.000000           NA       4           2
66   1.000000           NA       4           3
67   2.000000            3       4           4
68   1.000000            3       1           5
69   0.000000            3       1           6
70   1.000000            2       4           2
71   2.000000           NA       4           4
72   1.000000            2       4           1
73   0.000000           NA       4           2
74   1.000000            2       2           2
75   1.000000            3       4           4
76   0.000000           NA       4           1
77   0.000000           NA       4           1
78   0.000000           NA       4           4
79   1.000000           NA       4           5
80   0.000000            2       4           2
81   0.000000           NA       4           2
82   0.000000            2       4           4
83   1.000000           NA       4           5
84   0.000000           NA       5           1
85   1.000000            2       4           4
86   0.000000           NA       4           2
87   0.000000           NA       4           2
88   0.000000           NA       4           3
89   0.000000           NA       1           2
90   1.000000            3       1           4
91   0.000000           NA       4           6
92   1.000000            3       4           2
93   0.000000            3       5           3
94   0.000000           NA       5           2
95   0.000000           NA       4           2
96   0.000000            3       4           2
97   0.000000            2       4           2
98   0.000000            2       4           1
99   1.000000            3       4           2
100  0.000000            2       4           2
101  0.000000           NA       4           2
102  0.000000            2       1           2
103  0.000000            2       4           2
104  0.000000            2       1           5
105  0.000000            2       4           2
106  1.000000            2       1           3
107  2.000000           NA       5           5
108  1.000000           NA       4           5
109  1.000000            2       4           2
110  0.000000           NA       4           2
111  0.000000            2       4           2
112  0.000000           NA       5           2
113  0.000000            3       4           2
114  1.000000           NA       4           2
115  2.000000           NA       5           5
116  1.000000           NA       4           4
117  1.000000            2       4           2
118  1.000000            2       1           4
119  2.000000           NA       5           3
120  2.000000            3       4           4
121  0.000000           NA       5           1
122  0.000000           NA       4           2
123  0.000000           NA       4           4
124  0.000000            3       4           3
125  0.000000            2       4           2
126  0.000000            2       4           2
127  0.000000            2       4           2
128  1.000000            3       4           4
129  1.000000           NA       4           2
130  0.000000           NA       4           2
131  2.000000            3       4           2
132  1.000000            3       4           2
133  0.000000            2       1           5
134  0.000000            2       1           3
135  1.000000            2       4           2
136  0.000000           NA       3           5
137  0.000000           NA       2           2
138  1.000000           NA       4           2
139  1.000000            2       4           1
140  0.000000            3       4           2
141  1.000000            3       1           5
142  0.000000            2       1           3
143  2.000000            2       4           3
144  0.000000            2       4           3
145  2.000000            3       4           2
146  0.000000            2       1           5
147  0.000000            2       1           2
148  0.000000            2       1           5
149  1.000000            2       4           2
150  0.000000            2       1           2
151  0.000000            2       1           3
152  0.000000           NA       1           4
153  0.000000            2       4           5
154  1.000000            2       1           2
155  1.000000           NA       1           2
156  1.000000            2       4           2
157  0.000000            2       1           3
158  0.000000            3       1           4
159  1.000000            2       4           2
160  0.000000           NA       1           4
161  0.000000            2       4           2
162  0.000000           NA       4           2
163  1.000000            2       1           4
164  0.000000            2       4           6
165  0.000000            2       1           2
166  2.000000           NA       5           4
167  0.000000            2       4           3
168  0.000000           NA       1           4
169  1.000000            2       4           4
170  0.000000            2       4           2
171  2.000000            2       4           2
172  1.000000           NA       4           2
173  0.000000           NA       4           2
174  0.000000           NA       4           2
175  0.000000            2       4           2
176  2.000000            3       4           2
177  0.000000           NA       4           2
178  0.000000            2       4           2
179  1.000000           NA       4           1
180  0.000000            2       4           4
181  0.000000            2       1           3
182  0.000000            3       1           5
183  2.000000           NA       4           5
184  1.000000            3       1           5
185  1.000000           NA       4           2
186  0.000000            2       4           2
187  0.000000            2       4           3
188  0.000000            2       1           3
189  1.000000           NA       4           2
190  0.000000           NA       4           5
191  1.000000            2       5           3
192  2.000000            2       2           2
193  0.000000            2       2           6
194  2.000000           NA       4           1
195  1.000000            2       4           2
196  2.000000            2       4           5
197  0.000000            2       1           5
198  1.000000            2       4           7
199  0.000000            2       1           5
200  2.000000           NA       1           2
201  0.000000            3       4           5
202  0.000000           NA       4           5
203  0.000000            2       5           2
204  1.000000            2       4           2
205  1.000000           NA       4           6
206  1.000000           NA       4           2
207  0.000000           NA       4           4
208  0.000000           NA       4           2
209  0.000000            2       4           2
210  2.000000            2       4           2
211  0.000000            2       5           2
212  1.000000            3       1           3
213  0.000000           NA       4           2
214  0.000000            2       4           3
215  2.000000            3       4           3
216  1.000000            2       4           4
217  2.000000           NA       4           2
218  0.000000           NA       4           2
219  0.000000           NA       4           5
220  0.000000            2       5           6
221  1.000000           NA       1           2
222  1.000000            2       4           2
223  0.000000           NA       1           4
224  2.000000           NA       1           6
225  0.000000            2       5           3
226  0.000000            2       4           2
227  0.000000           NA       4           2
228  0.000000            2       1           4
229  0.000000           NA       4           2
230  0.000000           NA       4           2
231  0.000000           NA       1           2
232  1.000000           NA       4           2
233  1.000000           NA       4           2
234  1.000000           NA       4           2
235  1.000000           NA       4           2
236  0.000000           NA       2           3
237  2.000000           NA       1           2
238  0.000000            2       1           4
239  0.000000            2       4           2
240  1.000000            2       1           1
241  1.000000            2       4           2
242  1.000000           NA       1           2
243  0.000000            2       1           2
244  0.000000            2       5           3
245  1.000000           NA       4           4
246  0.000000            3       1           4
247  0.000000            3       1           4
248  2.000000            2       4           2
249  0.000000           NA       4           2
250  0.000000           NA       2           3
251  0.000000            2       4           4
252  2.000000           NA       4           5
253  0.000000           NA       4           4
254  1.000000            2       4           5
255  0.000000           NA       4           3
256  0.000000            2       5           3
257  1.000000            2       4           2
258  2.000000           NA       4           1
259  1.000000            2       1           2
260  1.000000            2       4           1
261  0.000000            2       4           1
262  0.000000            3       1           4
263  2.000000           NA       4           2
264  1.000000            2       5           2
265  1.000000            2       4           5
266  2.000000            2       4           2
267  1.000000           NA       4           2
268  0.000000            2       3           2
269  1.000000            2       4           2
270  1.000000            2       4           2
271  0.000000           NA       4           1
272  2.000000            2       5           2
273  0.000000            2       1           2
274  1.000000            2       4           1
275  0.000000            2       4           2
276  0.000000            2       4           2
277  1.000000           NA       4           2
278  1.000000            2       5           2
279  1.000000            2       4           2
280  0.000000            2       4           5
281  1.000000            2       4           2
282  0.000000           NA       4           2
283  2.000000           NA       4           2
284  0.000000            2       1           2
285  1.000000            2       4           2
286  1.000000           NA       5           1
287  1.000000           NA       4           2
288  0.000000           NA       4           2
289  2.000000            2       4           5
290  1.000000           NA       5           2
291  1.000000            2       4           2
292  0.000000           NA       1           3
293  1.000000            3       4           2
294  2.000000            2       4           3
295  0.000000            2       4           2
296  1.000000            2       4           2
297  2.000000            2       5           1
298  1.000000            2       4           2
299  1.000000            2       4           3
300  1.000000            2       4           6
301  2.000000            2       4           2
302  2.000000            2       4           2
303  0.000000            2       4           2
304  1.000000           NA       4           1
305  1.000000           NA       4           2
306  0.000000           NA       4           2
307  1.000000            2       4           2
308  0.000000           NA       5           2
309  0.000000            2       4           2
310  1.000000           NA       1           2
311  1.000000           NA       4           2
312  0.000000            2       4           2
313  1.000000            2       1           3
314  1.000000           NA       4           2
315  1.000000           NA       1           2
316  0.000000            2       4           1
317  0.000000            2       1           2
318  0.000000            2       4           3
319  0.000000            2       4           2
320  0.000000            2       4           2
321  0.000000            2       4           3
322  1.000000            2       5           2
323  1.000000            2       4           5
324  1.000000            2       4           1
325  1.000000           NA       4           2
326  0.000000           NA       5           2
327  0.000000           NA       1           2
328  2.000000            3       4           3
329  1.000000            2       4           1
330  1.000000           NA       4           2
331  2.000000           NA       5           2
332  2.000000            2       4           3
333  0.000000            3       4           1
334  1.000000           NA       4           2
335  0.000000            2       4           2
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337  0.000000           NA       4           2
338  1.000000            2       4           7
339  1.000000            2       4           2
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341  1.000000            2       5           4
342  2.000000           NA       5           2
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344  2.000000            2       5           2
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346  2.000000            2       5           2
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349  0.000000            2       4           3
350  1.000000           NA       5           1
351  1.000000           NA       1           2
352  0.000000            3       1           6
353  0.000000            2       4           2
354  1.000000            2       4           2
355  0.000000           NA       4           5
356  0.000000            2       1           3
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358  1.000000            2       4           3
359  0.000000            2       3           2
360  0.000000            2       1           4
361  1.000000            2       4           3
362  0.000000           NA       1           4
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365  2.000000            2       4           3
366  2.000000           NA       1           2
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370  0.000000            2       1           2
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375  0.000000            2       4           2
376  1.000000            2       4           2
377  1.000000            2       1           5
378  2.000000           NA       5           2
379  1.000000           NA       4           2
380  2.000000           NA       4           2
381  0.000000            2       3           5
382  0.000000           NA       4           5
383  0.000000           NA       4           3
384  2.000000           NA       4           1
385  1.000000            2       5           1
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387  1.000000            2       4           4
388  1.000000           NA       4           2
389  2.000000            2       4           1
390  1.000000            3       1           2
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392  0.000000            2       4           6
393  0.000000            2       3           2
394  0.000000           NA       4           3
395  1.000000            2       4           2
396  1.000000            2       5           4
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398  1.000000            2       1           2
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400  1.000000            2       1           2
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403  1.000000            2       4           2
404  1.000000           NA       4           2
405  0.000000            2       4           3
406  0.000000            2       1           2
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408  2.000000            3       1           5
409  1.000000           NA       5           2
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413  0.000000            2       4           5
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417  1.000000            2       5           4
418  2.000000            2       5           3
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420  1.000000           NA       4           2
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422  1.000000            2       4           2
423  0.000000            2       1           2
424  0.000000            2       4           5
425  1.000000            2       4           2
426  0.000000           NA       4           2
427  1.000000            2       4           2
428  1.000000            3       1           2
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430  0.000000            3       1           2
431  0.000000            2       5           2
432  1.000000            2       1           2
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436  1.000000            2       5           2
437  2.000000            2       4           1
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439  0.000000            2       4           2
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441  1.000000            2       4           3
442  2.000000            2       4           6
443  2.000000            2       4           2
444  1.000000            2       4           2
445  0.000000           NA       4           2
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449  1.000000            2       4           2
450  0.000000           NA       4           2
451  0.000000            2       4           2
452  1.000000            2       4           2
453  2.000000            3       4           3
454  2.000000           NA       4           5
455  0.000000            2       1           4
456  1.000000            2       4           2
457  0.000000            2       5           2
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459  0.000000           NA       1           2
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461  1.000000           NA       4           1
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464  1.000000           NA       4           4
465  0.000000            2       4           2
466  0.000000           NA       4           2
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468  0.000000           NA       4           3
469  1.000000           NA       4           2
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471  2.000000            2       4           2
472  1.000000            2       4           2
473  0.000000            2       4           2
474  1.000000            2       4           2
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476  2.000000           NA       1           4
477  1.000000            2       4           2
478  0.000000            3       4           2
479  0.000000           NA       3           5
480  1.000000            2       5           2
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483  1.000000            2       4           2
484  0.000000           NA       1           4
485  0.000000            2       3           2
486  0.000000            2       4           2
487  0.000000           NA       3           2
488  1.000000            2       4           2
489  1.000000            2       4           7
490  1.000000            2       4           7
491  0.294990            2       4           7
492  0.838957            2       4           7
493  0.479221            2       4           7
494  0.625350            2       4           7
495  0.265790            2       4           7
496  0.555468            2       4           7
497  0.928972            2       4           7
498  2.000000            2       4           1
499  1.340107            2       4           1
500  0.589980            2       4           1
501  1.000000           NA       4           1
502  1.000000           NA       4           1
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510  1.283673           NA       4           1
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514  0.000000            2       4           1
515  0.000000            2       4           1
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517  0.478676            2       4           1
518  0.555967            2       4           1
519  0.469735            2       4           1
520  0.371941            2       4           1
521  0.009254            2       1           1
522  0.832400            2       1           1
523  0.114716            2       1           1
524  1.000000           NA       4           1
525  0.256382           NA       4           1
526  1.000000           NA       4           1
527  1.547086            2       4           1
528  1.906141            2       4           1
529  1.639326            2       4           1
530  1.000000            2       4           1
531  1.000000            2       4           1
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533  2.000000           NA       4           1
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536  1.000000            2       4           1
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538  1.000000            2       4           1
539  0.000000           NA       4           1
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543  0.828549            2       4           1
544  0.474836            2       4           1
545  1.000000            2       4           1
546  1.000000            2       4           1
547  1.488372            2       4           1
548  0.556245           NA       1           1
549  1.894105           NA       1           1
550  1.839862           NA       1           1
551  1.380204            2       4           1
552  1.592570            2       4           1
553  1.273333            2       4           1
554  0.220029           NA       1           1
555  0.038380           NA       1           1
556  1.679149           NA       1           1
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558  1.009632           NA       4           1
559  0.152985           NA       4           1
560  1.000000            2       4           1
561  1.000000            2       4           1
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563  1.000000            2       4           1
564  1.000000            2       4           1
565  1.000000            2       4           1
566  0.000000            2       4           1
567  0.000000            2       4           1
568  0.635867            2       4           1
569  1.000000           NA       1           1
570  1.000000           NA       1           1
571  1.119877           NA       1           1
572  0.097234           NA       4           1
573  1.366355           NA       4           1
574  2.000000           NA       4           1
575  1.000000            2       4           1
576  1.000000            2       4           1
577  1.000000            2       4           1
578  2.000000            2       4           1
579  0.720454            2       4           1
580  0.322307            2       4           1
581  0.939819           NA       1           1
582  0.456462           NA       1           1
583  1.164457           NA       1           1
584  0.000000           NA       1           1
585  0.331483           NA       1           1
586  0.133005           NA       1           1
587  1.593704            2       4           1
588  1.329237            2       4           1
589  1.673584            2       4           1
590  0.000000            2       4           1
591  0.000000            2       4           1
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595  0.744555            2       4           1
596  1.708309            2       4           1
597  2.000000            2       4           1
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599  2.000000           NA       4           1
600  1.241755           NA       4           1
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605  0.196224            2       4           1
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609  1.679442            2       4           1
610  1.000000            2       4           1
611  0.814518           NA       4           1
612  1.000000            2       4           1
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614  0.369134            2       4           1
615  1.000000            2       4           1
616  0.930051           NA       1           1
617  1.466667            2       4           1
618  1.162801           NA       1           1
619  0.327376           NA       4           1
620  1.000000            2       4           1
621  0.704978            2       4           1
622  0.370973            2       4           1
623  0.560351           NA       1           1
624  2.000000           NA       4           1
625  1.525597            2       4           1
626  1.683261            2       4           1
627  0.000000           NA       1           1
628  0.128895           NA       1           1
629  1.884138            2       4           1
630  0.000000            2       4           1
631  0.907868            2       4           1
632  1.743319            2       4           1
633  1.894902           NA       4           1
634  1.612466           NA       1           1
635  0.844004           NA       4           1
636  1.250871           NA       4           1
637  1.000000           NA       4           1
638  1.391948           NA       4           1
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640  0.000000           NA       4           1
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642  0.000000           NA       4           1
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644  0.000000           NA       4           1
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648  0.504176            2       4           1
649  0.000000            2       4           1
650  0.751212            2       4           1
651  0.076654            2       4           1
652  0.646423           NA       4           1
653  0.128394           NA       4           1
654  0.861809            2       4           1
655  0.000355            2       1           1
656  0.267076            2       1           1
657  1.672508           NA       1           1
658  1.000000            2       4           1
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660  1.099764            2       4           1
661  1.001405            2       4           1
662  1.718513            2       4           1
663  1.064700            2       4           1
664  1.000000            2       4           1
665  1.000000            2       4           1
666  1.000000            2       4           1
667  1.990617           NA       4           1
668  2.000000           NA       4           1
669  1.561272           NA       4           1
670  1.000000            2       4           1
671  1.000000            2       4           1
672  1.000000            2       4           1
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678  0.858820            2       4           1
679  1.000000            2       4           1
680  1.000000            2       4           1
681  1.258881            2       4           1
682  0.453649           NA       1           1
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684  1.843830           NA       1           1
685  1.253311           NA       4           1
686  1.550110            2       4           1
687  1.403037            2       4           1
688  0.378619           NA       1           1
689  0.174848           NA       1           1
690  1.807465           NA       1           1
691  0.417116           NA       4           1
692  1.425903           NA       4           1
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694  1.000000            2       4           1
695  0.956204            2       4           1
696  1.000000            2       4           1
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699  1.000000            2       4           1
700  0.000000            2       4           1
701  0.000000            2       4           1
702  0.773087            2       4           1
703  1.000000           NA       1           1
704  0.714701           NA       1           1
705  0.613971           NA       1           1
706  0.089220           NA       4           1
707  1.300692           NA       4           1
708  1.958089           NA       4           1
709  1.000000            2       4           1
710  1.000000            2       4           1
711  1.000000            2       4           1
712  1.972926            2       4           1
713  1.626194            2       4           1
714  0.403694            2       4           1
715  1.510609           NA       1           1
716  0.384662           NA       1           1
717  1.128355           NA       1           1
718  0.007872           NA       1           1
719  0.588994           NA       1           1
720  0.039210           NA       1           1
721  1.608265            2       4           1
722  1.672532            2       4           1
723  1.151166            2       4           1
724  0.000000            2       4           1
725  0.000000            2       4           1
726  0.000000            2       4           1
727  0.513427            2       4           1
728  0.800993            2       4           1
729  0.560887            2       4           1
730  1.747256            2       4           1
731  2.000000            2       4           1
732  1.487223            2       4           1
733  0.554072           NA       1           3
734  0.000000            2       4           3
735  1.000000           NA       1           3
736  0.000000            2       4           3
737  0.479221            2       4           3
738  0.374650            2       4           3
739  0.265790            2       1           3
740  1.000000            2       4           3
741  0.142056            2       4           3
742  0.707044            3       4           3
743  1.415536            2       4           3
744  0.000000            2       1           3
745  1.358163            2       4           3
746  0.000000            2       4           3
747  0.997400            2       1           3
748  0.369134            2       1           3
749  0.000000            2       4           3
750  0.000000            2       4           3
751  0.000000            2       1           3
752  0.000000            2       1           3
753  0.469735            2       4           3
754  0.628059            2       4           3
755  0.009254            2       4           3
756  0.335200            2       4           3
757  2.000000            2       4           3
758  0.315918            2       4           3
759  0.000000            2       4           3
760  0.000000            2       4           3
761  0.000000           NA       4           3
762  0.000000            2       4           3
763  0.000000            2       1           3
764  0.879977            2       4           3
765  1.893880            2       4           3
766  0.196680            2       4           3
767  0.273882            2       4           3
768  1.339232            2       1           3
769  1.597608            2       4           3
770  1.944675            2       4           3
771  1.056911            2       4           3
772  1.887386            2       4           3
773  0.000000            2       1           3
774  1.000000            2       1           3
775  0.480813            2       1           3
776  1.285373            2       4           3
777  1.657098           NA       4           3
778  0.000000           NA       4           3
779  0.008899            2       4           3
780  1.720642            2       4           3
781  0.000000           NA       1           3
782  1.112489            2       1           3
783  0.105895           NA       4           3
784  0.160138            2       4           3
785  1.690102            3       4           3
786  1.592570           NA       4           3
787  1.453335            3       4           3
788  0.000000            2       4           3
789  0.000000           NA       1           3
790  0.096614           NA       1           3
791  0.000000            2       4           3
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793  1.000000           NA       1           3
794  0.117303            2       4           3
795  0.426643            2       4           3
796  1.194633            2       4           3
797  0.004813            2       4           3
798  0.142638            2       4           3
799  0.000000            2       1           3
800  1.000000            2       4           3
801  0.798219            2       4           3
802  1.983678            2       4           3
803  2.000000            2       4           3
804  0.961806            3       4           3
805  0.366126            2       4           3
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808  1.376124            2       4           3
809  1.980875            2       4           3
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811  0.360193            2       1           3
812  0.305954            2       1           3
813  0.000000            2       4           3
814  0.000000            2       4           3
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816  0.686529            2       1           3
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818  0.000000            2       4           3
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820  0.143675            2       4           3
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822  0.678618            2       4           3
823  0.673835            2       4           3
824  0.307982            2       4           3
825  0.459759            2       4           3
826  1.650778            2       4           3
827  0.967919            2       4           3
828  0.622638            2       4           3
829  0.426465            2       4           3
830  0.014885            2       4           3
831  0.000000            2       4           3
832  0.000000            2       4           3
833  0.817983           NA       4           3
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835  0.000000            2       4           3
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837  0.894678            2       4           3
838  0.022245            2       4           3
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840  0.561602            2       4           3
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843  0.000000            2       1           3
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847  1.344072           NA       4           3
848  0.000000            2       4           3
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850  0.000000            2       1           3
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854  1.765321           NA       4           3
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866  0.871772            2       4           3
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868  2.000000            3       4           3
869  1.409198            3       4           3
870  1.899073           NA       4           3
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894  0.000000            2       4           3
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908  0.492528            2       4           3
909  2.000000            2       4           3
910  1.466496            2       4           3
911  0.208323            2       4           3
912  0.271024            2       4           3
913  0.000000            2       4           3
914  1.102696            2       4           3
915  0.000000            2       4           3
916  0.681730           NA       4           3
917  0.000000            2       4           3
918  0.000000            2       4           3
919  0.000000            2       1           3
920  0.939726           NA       4           3
921  0.888680           NA       4           3
922  1.502839            2       4           3
923  0.311436            2       4           3
924  0.150969            2       4           3
925  1.301385            2       1           3
926  1.898139            2       4           3
927  1.827530            2       4           3
928  1.969507            2       4           3
929  1.915191            2       4           3
930  1.165817           NA       4           3
931  1.618227            2       4           3
932  0.000000            2       1           3
933  1.000000            2       1           3
934  0.435072            2       1           3
935  1.110222            2       4           3
936  1.772463           NA       4           3
937  1.890214           NA       4           3
938  0.000000           NA       4           3
939  0.001096            2       4           3
940  0.003229            2       4           3
941  1.753745            2       4           3
942  1.619473            2       4           3
943  0.370067           NA       1           3
944  0.916291            2       1           3
945  0.303025            2       1           3
946  0.017225            3       4           3
947  0.226097            2       4           3
948  0.393358            2       4           3
949  1.801322           NA       4           3
950  1.626456           NA       4           3
951  1.405086            3       4           3
952  0.797215            3       4           3
953  0.908836            2       4           3
954  0.973834            2       4           4
955  0.000000            2       4           4
956  1.443212           NA       4           4
957  1.000000           NA       4           4
958  0.000000            2       5           4
959  0.000000            2       1           4
960  0.987102            2       1           4
961  1.409130           NA       4           4
962  2.000000           NA       4           4
963  1.581703            2       4           4
964  0.999183            2       1           4
965  0.854050            2       1           4
966  1.323877           NA       4           4
967  0.509396           NA       4           4
968  0.786609            2       4           4
969  1.000000           NA       4           4
970  1.000000           NA       4           4
971  1.365950            2       4           4
972  0.552498            2       4           4
973  1.540298           NA       4           4
974  1.611271           NA       4           4
975  1.000000            2       4           4
976  1.000000            2       4           4
977  0.658783            2       4           4
978  0.453404            2       4           4
979  0.322405            2       4           4
980  1.000000            3       1           4
981  0.761898           NA       4           4
982  0.785190            2       4           4
983  1.939770            2       4           4
984  1.541493            2       4           4
985  0.947192            2       4           4
986  1.922364            2       4           4
987  1.000000            3       4           4
988  0.000000           NA       1           4
989  0.434198            3       4           4
990  0.000000           NA       1           4
991  0.891189           NA       1           4
992  2.000000           NA       4           4
993  0.854536           NA       4           4
994  1.481890           NA       1           4
995  1.506576            2       4           4
996  0.000000           NA       4           4
997  0.000000           NA       4           4
998  0.613902           NA       4           4
999  0.890626           NA       4           4
1000 1.043435            2       4           4
1001 1.127675            2       4           4
1002 0.000000           NA       1           4
1003 1.517897           NA       1           4
1004 0.681773           NA       1           4
1005 0.999268           NA       1           4
1006 0.519395            2       1           4
1007 0.000000            2       1           4
1008 1.000000            2       4           4
1009 1.000000            2       4           4
1010 0.133566           NA       4           4
1011 0.000000           NA       1           4
1012 0.417580            2       4           4
1013 0.429081            2       4           4
1014 0.938791            2       4           4
1015 0.000000           NA       1           4
1016 0.651904            2       4           4
1017 0.000000           NA       1           4
1018 0.845774           NA       1           4
1019 0.000000           NA       1           4
1020 0.440087            2       4           4
1021 1.000000            2       4           4
1022 0.075823            2       1           4
1023 0.203978            2       4           4
1024 1.000000            2       4           4
1025 1.000000            2       4           4
1026 1.000000            2       4           4
1027 0.084006            2       4           4
1028 0.648522            2       4           4
1029 0.000000           NA       1           4
1030 0.155278            2       4           4
1031 0.276364            2       4           4
1032 0.663649            2       4           4
1033 0.739609            2       4           4
1034 0.688013            2       4           4
1035 1.000000           NA       4           4
1036 1.503010           NA       1           4
1037 0.707246           NA       1           4
1038 0.173665            2       4           4
1039 0.000000           NA       1           4
1040 0.000000           NA       1           4
1041 0.306124            2       4           4
1042 1.269982           NA       4           4
1043 0.000000            2       4           4
1044 0.000000           NA       1           4
1045 1.692287           NA       4           4
1046 0.333673           NA       4           4
1047 0.020153            2       1           4
1048 1.444532           NA       4           4
1049 0.000000            2       4           4
1050 1.228995           NA       4           4
1051 0.975504            2       4           4
1052 1.732160           NA       4           4
1053 0.799756            2       4           4
1054 0.124977           NA       4           4
1055 0.000000            2       4           4
1056 0.630866            2       4           4
1057 1.000000            3       1           4
1058 0.962468            2       4           4
1059 1.000000            2       4           4
1060 1.971170            2       4           4
1061 1.066708            2       4           4
1062 1.000000            3       1           4
1063 0.418623           NA       1           4
1064 0.371941            3       1           4
1065 0.000000           NA       1           4
1066 0.832400           NA       1           4
1067 1.114716           NA       4           4
1068 0.000000           NA       1           4
1069 1.094941            2       4           4
1070 0.969097           NA       4           4
1071 0.000000           NA       4           4
1072 1.011983            2       4           4
1073 0.000000           NA       1           4
1074 0.879333           NA       1           4
1075 0.000000            2       1           4
1076 0.827439            2       4           4
1077 0.726118            2       4           4
1078 0.000000           NA       1           4
1079 0.608607            2       4           4
1080 0.003695           NA       4           4
1081 0.000000           NA       1           4
1082 1.886855            2       4           4
1083 0.000000           NA       1           4
1084 0.790967           NA       1           4
1085 1.000000            2       4           4
1086 0.027093            2       1           4
1087 0.828549            2       4           4
1088 0.965464            2       4           4
1089 1.000000            2       4           4
1090 0.860321            2       4           4
1091 0.000000           NA       1           4
1092 0.746651            2       4           4
1093 0.105895            2       4           4
1094 0.464022            2       4           4
1095 1.000000           NA       4           4
1096 1.796136           NA       1           4
1097 0.688293            2       4           4
1098 0.779334           NA       1           4
1099 0.568668            2       4           4
1100 0.000000            2       4           4
1101 1.413375           NA       4           4
1102 1.206535           NA       4           4
1103 0.000000            2       1           4
1104 0.000000           NA       1           4
1105 0.395979           NA       1           4
1106 1.343044           NA       4           4
1107 2.000000           NA       4           4
1108 1.019452           NA       4           4
1109 0.673408           NA       1           4
1110 0.997600            2       1           4
1111 1.313363           NA       4           4
1112 0.575848           NA       4           4
1113 0.441502            2       4           4
1114 1.000000           NA       4           4
1115 1.119877           NA       4           4
1116 1.895312            2       4           4
1117 0.819475            2       4           4
1118 1.462664           NA       4           4
1119 1.544357           NA       4           4
1120 1.000000            2       4           4
1121 0.089354            2       4           4
1122 0.908271            3       4           4
1123 0.163329            2       4           4
1124 1.344030            2       4           4
1125 0.839659            3       1           4
1126 0.604730            2       4           4
1127 0.328030            2       4           4
1128 1.944177            2       4           4
1129 1.667745            2       4           4
1130 0.954216            2       4           4
1131 1.365793            2       4           4
1132 1.029135            2       4           4
1133 0.290898           NA       1           4
1134 0.524015            3       4           4
1135 0.000000           NA       1           4
1136 0.936925           NA       1           4
1137 2.000000           NA       4           4
1138 0.864583           NA       4           4
1139 1.103349           NA       1           4
1140 1.358812           NA       4           4
1141 0.000000           NA       4           4
1142 0.235711           NA       4           4
1143 0.517430           NA       4           4
1144 1.261043           NA       4           4
1145 1.749586           NA       4           4
1146 0.544899            2       4           4
1147 0.000000           NA       1           4
1148 0.721167           NA       1           4
1149 0.704637           NA       1           4
1150 1.040787           NA       1           4
1151 0.481357            2       1           4
1152 0.000000            2       1           4
1153 1.000000           NA       4           4
1154 1.000000            2       4           4
1155 0.518542            2       4           4
1156 0.000000           NA       1           4
1157 0.121992            2       4           4
1158 0.426830            2       4           4
1159 0.971746           NA       4           4
1160 0.107240           NA       1           4
1161 0.933802            2       4           4
1162 0.000000           NA       1           4
1163 0.905181           NA       1           4
1164 0.000000           NA       1           4
1165 0.805008            2       4           4
1166 1.000000            2       4           4
1167 0.017016            2       1           4
1168 0.176892            2       4           4
1169 1.212762           NA       4           4
1170 0.646514            2       4           4
1171 0.934493            2       4           4
1172 0.487375            2       4           4
1173 0.880414            2       4           4
1174 0.000000           NA       1           4
1175 0.037634            2       4           4
1176 0.218574            2       4           4
1177 0.606264            2       1           4
1178 0.632467            2       4           4
1179 0.993786            2       4           4
1180 1.000000           NA       4           4
1181 1.528714           NA       1           4
1182 0.691944           NA       1           4
1183 0.023564            2       4           4
1184 0.000000           NA       1           4
1185 0.000000           NA       1           4
1186 1.619596            2       4           5
1187 1.564796            2       4           5
1188 0.413752            2       1           5
1189 1.000000            2       1           5
1190 0.000000            2       4           5
1191 0.000000            2       4           5
1192 0.000000           NA       1           5
1193 0.000000           NA       1           5
1194 1.482016            2       4           5
1195 2.000000            2       4           5
1196 1.338655           NA       4           5
1197 1.000000           NA       4           5
1198 1.000000           NA       4           5
1199 0.070897           NA       4           5
1200 0.000000            2       1           5
1201 1.237867           NA       4           5
1202 2.000000           NA       4           5
1203 1.777950           NA       4           5
1204 1.350099            2       4           5
1205 0.000000            2       1           5
1206 0.000000            2       1           5
1207 1.000000           NA       4           5
1208 0.000000            2       1           5
1209 0.000000            2       1           5
1210 0.105936           NA       4           5
1211 0.522259            2       1           5
1212 2.000000           NA       4           5
1213 2.000000           NA       4           5
1214 0.092736            3       1           5
1215 1.281141           NA       4           5
1216 0.000000           NA       4           5
1217 1.707018           NA       4           5
1218 0.000000            2       1           5
1219 0.000000            2       1           5
1220 0.505266            2       1           5
1221 0.000000           NA       4           5
1222 0.000000           NA       4           5
1223 1.206251           NA       4           5
1224 0.000000           NA       4           5
1225 0.526999            2       4           5
1226 2.000000            2       4           5
1227 0.732265            2       4           5
1228 1.000000            2       4           5
1229 0.508663            2       4           5
1230 0.769060            2       4           5
1231 0.000000           NA       4           5
1232 0.621605           NA       4           5
1233 1.609773           NA       4           5
1234 1.441605           NA       4           5
1235 1.616826            2       4           5
1236 0.128681            2       4           5
1237 0.000000            2       4           5
1238 1.000000            2       4           5
1239 1.000000            2       4           5
1240 0.000000            2       1           5
1241 0.000000            2       1           5
1242 0.233314           NA       4           5
1243 0.000000            2       1           5
1244 0.000000            2       1           5
1245 0.676557            2       4           5
1246 0.103056            2       4           5
1247 1.817146            2       1           5
1248 0.000000           NA       4           5
1249 0.329367            2       1           5
1250 0.000000            2       1           5
1251 0.000000           NA       4           5
1252 1.595306           NA       4           5
1253 1.882539           NA       4           5
1254 0.537202           NA       4           5
1255 0.000000           NA       4           5
1256 1.758865            2       4           5
1257 0.680215            2       4           5
1258 0.360370            3       1           5
1259 0.853891            3       1           5
1260 0.000000           NA       4           5
1261 0.000000           NA       4           5
1262 0.000000           NA       1           5
1263 0.000000           NA       1           5
1264 1.727828            2       4           5
1265 0.585912            2       4           5
1266 1.000000           NA       4           5
1267 1.066603           NA       4           5
1268 1.000000           NA       4           5
1269 0.969085           NA       4           5
1270 0.002600           NA       4           5
1271 0.630866           NA       4           5
1272 0.000000            2       1           5
1273 0.000000            2       1           5
1274 2.000000           NA       4           5
1275 2.000000           NA       4           5
1276 1.000000           NA       4           5
1277 1.000000           NA       4           5
1278 1.060530           NA       4           5
1279 1.256119           NA       4           5
1280 1.604608            2       1           5
1281 0.000000            2       1           5
1282 0.000000           NA       1           5
1283 0.000000           NA       1           5
1284 0.335158           NA       4           5
1285 1.000000           NA       4           5
1286 0.000000            2       1           5
1287 0.000000            2       1           5
1288 0.000000            2       1           5
1289 0.000000            2       1           5
1290 0.169294           NA       4           5
1291 1.281541           NA       4           5
1292 0.000000            2       1           5
1293 0.669616            2       1           5
1294 1.365950           NA       4           5
1295 1.978043           NA       4           5
1296 0.572185            2       1           5
1297 1.771135            2       1           5
1298 0.000000           NA       4           5
1299 0.889517           NA       4           5
1300 1.731070            2       4           5
1301 2.000000            2       4           5
1302 0.000000            2       1           5
1303 0.000000            2       1           5
1304 0.000000            2       1           5
1305 0.000000            2       1           5
1306 0.000000           NA       4           5
1307 0.000000           NA       4           5
1308 0.894105           NA       4           5
1309 0.839862           NA       4           5
1310 0.459274           NA       4           5
1311 0.301909           NA       4           5
1312 2.000000            2       4           5
1313 1.220029            2       4           5
1314 1.000000            2       4           5
1315 0.320851            2       4           5
1316 0.569852            2       4           5
1317 0.808980            2       4           5
1318 0.077818            2       4           5
1319 0.000000            2       4           5
1320 0.802305           NA       4           5
1321 1.676944           NA       4           5
1322 1.554404           NA       4           5
1323 0.186414           NA       4           5
1324 0.000000            2       4           5
1325 0.000000            2       4           5
1326 1.014808            2       4           5
1327 1.000000            2       4           5
1328 0.000000            2       1           5
1329 0.000000            2       1           5
1330 1.119877           NA       4           5
1331 0.048617           NA       4           5
1332 0.000000            2       1           5
1333 0.000000            2       1           5
1334 0.169879           NA       4           5
1335 0.434600           NA       4           5
1336 0.178708            2       1           5
1337 0.537659            2       1           5
1338 0.468483            2       4           5
1339 0.838847            2       4           5
1340 0.000000            2       1           5
1341 0.000000            2       1           5
1342 0.786066           NA       4           5
1343 0.000000           NA       4           5
1344 1.865851           NA       4           5
1345 1.946173           NA       4           5
1346 0.548539           NA       4           5
1347 0.905596           NA       4           5
1348 0.202902           NA       4           5
1349 0.000000           NA       4           5
1350 1.584830            2       4           5
1351 1.596562            2       4           5
1352 1.594050            2       4           5
1353 1.509831            2       4           5
1354 0.707780            2       1           5
1355 0.584755            2       1           5
1356 1.000000            3       1           5
1357 0.616045            3       1           5
1358 0.000000            2       1           5
1359 0.000000            2       1           5
1360 1.094046           NA       4           5
1361 0.078607           NA       4           5
1362 0.000000           NA       1           5
1363 0.000000           NA       1           5
1364 0.000000           NA       1           5
1365 0.000000           NA       1           5
1366 1.382906            2       4           5
1367 1.516731            2       4           5
1368 2.000000            2       4           5
1369 0.289218            2       4           5
1370 1.632506           NA       4           5
1371 1.529423           NA       4           5
1372 1.000000           NA       4           5
1373 1.000000           NA       4           5
1374 1.000000           NA       4           5
1375 1.000000           NA       4           5
1376 0.013483           NA       4           5
1377 0.167728           NA       4           5
1378 0.000000            2       1           5
1379 0.000000            2       1           5
1380 1.082838           NA       4           5
1381 0.212767           NA       4           5
1382 2.000000           NA       4           5
1383 2.000000           NA       4           5
1384 1.612432           NA       4           5
1385 1.460564           NA       4           5
1386 1.252677           NA       4           5
1387 1.195929           NA       4           5
1388 1.032392            2       4           5
1389 0.133523            2       4           5
1390 0.000000            2       1           5
1391 0.000000            2       1           5
1392 0.733221           NA       1           5
1393 0.615990           NA       1           5
1394 0.000000            2       1           5
1395 0.000000            2       1           5
1396 0.000000            2       1           5
1397 0.000000            2       1           5
1398 1.393220           NA       4           5
1399 1.292476           NA       4           5
1400 0.903903            3       1           5
1401 0.732881            3       1           5
1402 1.287117           NA       4           5
1403 2.000000           NA       4           5
1404 2.000000           NA       4           5
1405 1.402414           NA       4           5
1406 0.317846            2       1           5
1407 0.105408            2       1           5
1408 0.195339           NA       4           5
1409 1.426103           NA       4           5
1410 0.896185           NA       4           5
1411 0.196084           NA       4           5
1412 1.659476            2       4           5
1413 0.406990            2       4           5
1414 0.000000            2       1           5
1415 0.000000            2       1           5
1416 0.000000            2       1           5
1417 0.000000            2       1           5
1418 0.920858            2       1           5
1419 0.122280            2       1           5
1420 0.000000           NA       4           5
1421 0.000000           NA       4           5
1422 0.000000           NA       4           5
1423 0.000000           NA       4           5
1424 1.224737           NA       4           5
1425 1.229474           NA       4           5
1426 0.000000           NA       4           5
1427 0.000000           NA       4           5
1428 0.544128            2       4           5
1429 0.175587            2       4           5
1430 1.157466            2       4           5
1431 2.000000            2       4           5
1432 0.363549            2       4           5
1433 0.920162            2       4           5
1434 0.518297            2       4           5
1435 0.831412            2       4           5
1436 0.175694            2       4           5
1437 0.413220            2       4           5
1438 0.676880            2       4           5
1439 0.579541            2       4           5
1440 0.000000            2       4           5
1441 0.000000            2       4           5
1442 0.756339            2       4           5
1443 0.261793            2       4           5
1444 1.546738           NA       4           5
1445 0.741069           NA       4           5
1446 1.475740           NA       4           5
1447 1.541455           NA       4           5
1448 1.376597            2       4           5
1449 1.560402            2       4           5
1450 0.061282           NA       4           5
1451 0.598244           NA       4           5
1452 0.000000            2       4           5
1453 0.144510            2       4           5
1454 1.223661            2       4           5
1455 1.285838            2       4           5
1456 1.000000            2       4           5
1457 1.914531            2       4           5
1458 0.000000            2       1           5
1459 0.000000            2       1           5
1460 0.000000            2       1           5
1461 0.000000            2       1           5
1462 0.467048           NA       4           5
1463 0.931721           NA       4           5
1464 0.000000            2       1           5
1465 0.000000            2       1           5
1466 0.000000            2       1           5
1467 0.000000            2       1           5
1468 0.652485           NA       4           5
1469 0.552665           NA       4           5
1470 1.425852           NA       4           5
1471 0.645400           NA       4           5
1472 1.767468            2       1           5
1473 1.670313            2       1           5
1474 0.000000           NA       4           5
1475 0.000000           NA       4           5
1476 0.221830           NA       1           5
1477 0.885532           NA       1           5
1478 0.000000            2       1           5
1479 0.319008            2       1           5
1480 0.313016           NA       4           5
1481 0.304020           NA       4           5
1482 1.355370           NA       4           5
1483 1.331527           NA       4           5
1484 1.809310           NA       4           5
1485 1.917679           NA       4           5
1486 0.951740           NA       4           5
1487 0.720411           NA       4           5
1488 0.000000           NA       4           5
1489 0.000000           NA       4           5
1490 1.108145           NA       1           6
1491 0.000000           NA       1           6
1492 0.000000           NA       4           6
1493 0.000000           NA       4           6
1494 0.000000            2       4           6
1495 0.000000            2       4           6
1496 0.000000            2       1           6
1497 0.000000            2       1           6
1498 1.928972            2       4           6
1499 0.292956            2       4           6
1500 1.415536            2       4           6
1501 0.000000            2       4           6
1502 1.358163            2       1           6
1503 1.875023            2       1           6
1504 0.002600            2       4           6
1505 0.630866            2       4           6
1506 0.000000            2       1           6
1507 0.315261            2       1           6
1508 0.000000            2       4           6
1509 0.000000            2       4           6
1510 0.890213           NA       4           6
1511 1.444033           NA       4           6
1512 0.613992            2       1           6
1513 0.001716            2       1           6
1514 0.000000           NA       4           6
1515 0.000000           NA       4           6
1516 0.976473            2       4           6
1517 0.000000            2       4           6
1518 0.360986            2       1           6
1519 0.754417            2       1           6
1520 1.000000            2       4           6
1521 0.739006            2       4           6
1522 0.707768            2       4           6
1523 0.396352            2       4           6
1524 0.716327            2       1           6
1525 1.875023            2       1           6
1526 0.631825            2       4           6
1527 0.863740            2       4           6
1528 0.000000           NA       1           6
1529 0.000000           NA       1           6
1530 0.000000            2       4           6
1531 0.000000            2       4           6
1532 1.384186           NA       4           6
1533 1.690901           NA       4           6
1534 1.493716            2       1           6
1535 0.696948            2       1           6
1536 0.000000            2       4           6
1537 0.000436            2       4           6
1538 0.000000           NA       1           6
1539 0.000000           NA       1           6
1540 0.000000           NA       4           6
1541 1.073900           NA       4           6
1542 0.424612            2       4           6
1543 0.000000            2       4           6
1544 0.489854            2       1           6
1545 1.759954            2       1           6
1546 0.000000            2       4           6
1547 0.000000            2       4           6
1548 0.000000            2       1           6
1549 0.947866            2       1           6
1550 0.390178            2       4           6
1551 1.875683            2       4           6
1552 0.677354            2       1           6
1553 1.836853            2       1           6
1554 0.738935            2       4           6
1555 0.677909            2       4           6
1556 0.000000            2       4           6
1557 0.357314            2       4           6
1558 0.232858            2       1           6
1559 1.035711            2       1           6
1560 1.360463            2       4           6
1561 0.261901            2       4           6
1562 0.001330            2       4           6
1563 0.857438            2       4           6
1564 0.066805            2       4           6
1565 0.101026            2       4           6
1566 0.000000            2       1           6
1567 0.000000            2       1           6
1568 1.135503           NA       4           6
1569 0.069367           NA       4           6
1570 0.000000            2       4           6
1571 0.428452            2       4           6
1572 0.000000            2       4           6
1573 0.504816            2       4           6
1574 0.974939           NA       4           6
1575 0.620465           NA       4           6
1576 0.768071           NA       4           6
1577 1.261705           NA       4           6
1578 0.340196            2       1           6
1579 0.731257            2       1           6
1580 0.687342           NA       4           6
1581 0.157007           NA       4           6
1582 0.000000            2       4           6
1583 0.000000            2       4           6
1584 0.200516            2       1           6
1585 0.419055            2       1           6
1586 0.072264            2       4           6
1587 0.739684            2       4           6
1588 0.500936            2       4           6
1589 0.100048            2       4           6
1590 1.588046            2       1           6
1591 1.992190            2       1           6
1592 0.002600            2       4           6
1593 0.630866            2       4           6
1594 0.000000           NA       1           6
1595 0.000000           NA       1           6
1596 0.000000            2       4           6
1597 0.000000            2       4           6
1598 1.431200           NA       4           6
1599 1.382995           NA       4           6
1600 0.725222            2       1           6
1601 1.251554            2       1           6
1602 0.849503            2       4           6
1603 0.000073            2       4           6
1604 0.000000           NA       1           6
1605 0.000000           NA       1           6
1606 0.000000           NA       4           6
1607 1.040713           NA       4           6
1608 0.000000            2       4           6
1609 0.000000            2       4           6
1610 0.176678            2       1           6
1611 0.000000            2       1           6
1612 0.010721            2       4           6
1613 0.081156            2       4           6
1614 0.262118            2       1           6
1615 1.339232            2       1           6
1616 1.073026            2       4           6
1617 1.899330            2       4           6
1618 0.474216            2       1           6
1619 1.749920            2       1           6
1620 1.191998            2       4           6
1621 0.677909            2       4           6
1622 0.000000            2       4           6
1623 0.127673            2       4           6
1624 1.090995            2       1           6
1625 1.480875            2       1           6
1626 0.008343            2       4           6
1627 1.432336            2       4           6
1628 0.002168            2       4           6
1629 0.001640            2       4           6
1630 0.449886            2       4           6
1631 0.460866            2       4           6
1632 0.000000            2       1           6
1633 0.000000            2       1           6
1634 1.003011           NA       4           6
1635 0.553311           NA       4           6
1636 0.000000            2       4           6
1637 0.000000            2       4           6
1638 0.000000            2       4           6
1639 0.000000            2       4           6
1640 0.754023           NA       4           6
1641 0.715993           NA       4           6
1642 1.163117           NA       4           6
1643 1.368262           NA       4           6
1644 0.325445            2       1           6
1645 0.114457            2       1           6
1646 0.001590            2       4           6
1647 0.001337            2       4           6
1648 0.000000           NA       4           6
1649 0.394533           NA       4           6
1650 0.138314           NA       4           6
1651 0.051268           NA       4           6
1652 1.022505            2       4           6
1653 0.047473            2       4           6
1654 0.310394            2       4           6
1655 0.111925            2       4           6
1656 0.345887            2       1           6
1657 1.333559            2       1           6
1658 0.067410            2       1           6
1659 0.202810            2       1           6
1660 0.764452            2       4           6
1661 0.690353            2       4           6
1662 0.391740            2       4           6
1663 0.690082            2       4           6
1664 0.295685            2       4           6
1665 0.682128            2       4           6
1666 0.264969            2       4           6
1667 0.343635            2       4           6
1668 1.140348            2       1           6
1669 1.416353            2       1           6
1670 1.915818            2       1           6
1671 1.668318            2       1           6
1672 0.631217            2       4           6
1673 0.631422            2       4           6
1674 0.936480            2       4           6
1675 0.838947            2       4           6
1676 0.000000           NA       1           6
1677 0.000000           NA       1           6
1678 0.000000           NA       4           6
1679 0.412329           NA       4           6
1680 0.000000            2       4           6
1681 0.000000            2       4           6
1682 0.000000            2       4           6
1683 0.000000            2       4           6
1684 1.098380           NA       4           6
1685 1.707421           NA       4           6
1686 0.862587           NA       4           6
1687 1.665621           NA       4           6
1688 1.465479            2       1           6
1689 1.439004            2       1           6
1690 0.511694            2       1           6
1691 0.062167            2       1           6
1692 0.380979            2       4           6
1693 0.247332            2       4           6
1694 0.276319            2       4           6
1695 0.076693            2       4           6
1696 0.000000            2       1           6
1697 0.000000            2       1           6
1698 0.000000           NA       1           6
1699 0.000000           NA       1           6
1700 0.413663           NA       4           6
1701 0.000000           NA       4           6
1702 0.778375           NA       4           6
1703 0.386861           NA       4           6
1704 1.150439            2       4           6
1705 0.917727            2       4           6
1706 0.090341            2       4           6
1707 0.173232            2       4           6
1708 0.593917            2       1           6
1709 0.376683            2       1           6
1710 0.921991            2       1           6
1711 1.556052            2       1           6
1712 0.000000            2       4           6
1713 0.000000            2       4           6
1714 0.026575            2       4           6
1715 0.038253            2       4           6
1716 0.000000            2       1           6
1717 0.000000            2       1           6
1718 0.972054            2       1           6
1719 0.785701            2       1           6
1720 0.052942            2       4           6
1721 0.079334            2       4           6
1722 0.688985            2       4           6
1723 1.220767            2       4           6
1724 0.618087            2       1           6
1725 0.847587            2       1           6
1726 1.742880            2       1           6
1727 1.578517            2       1           6
1728 0.555591            2       4           6
1729 0.758897            2       4           6
1730 1.339691            2       4           6
1731 0.188693            2       4           6
1732 0.000000            2       4           6
1733 0.000000            2       4           6
1734 0.281815            2       4           6
1735 0.063005            2       4           6
1736 1.198439            2       1           6
1737 0.291600            2       1           6
1738 0.477870            2       1           6
1739 1.152899            2       1           6
1740 0.217632            2       4           6
1741 0.527766            2       4           6
1742 0.185499            2       4           6
1743 1.015467            2       4           6
1744 0.001867            2       4           6
1745 0.001518            2       4           6
1746 0.356868            2       4           6
1747 0.228598            2       4           6
1748 0.020044            2       4           6
1749 0.145284            2       4           6
1750 0.196952            2       4           6
1751 0.078207            2       4           6
1752 0.000000            2       1           6
1753 0.646760            2       1           6
1754 0.000000            2       1           6
1755 0.000000            2       1           6
1756 1.551934           NA       4           6
1757 0.619012           NA       4           6
1758 0.023959           NA       4           6
1759 0.056351           NA       4           6
1760 0.442456            2       4           6
1761 0.000000            2       4           6
1762 0.531769            2       4           6
1763 0.150544            2       4           6
1764 0.362160            2       4           6
1765 0.000000            2       4           6
1766 0.315595            2       4           6
1767 0.034897            2       4           6
1768 1.058007           NA       4           6
1769 1.217929           NA       4           6
1770 0.378818           NA       4           6
1771 0.636289           NA       4           6
1772 1.122118           NA       4           6
1773 0.819980           NA       4           6
1774 1.239038           NA       4           6
1775 1.041677           NA       4           6
1776 1.000000            2       4           7
1777 0.534769            2       4           7
1778 0.735868            2       4           7
1779 0.939665            2       4           7
1780 0.138418            2       4           7
1781 0.374650            2       4           7
1782 0.734210            2       4           7
1783 0.444532            2       4           7
1784 0.317940            2       4           7
1785 0.886135            2       4           7
1786 0.479348            2       4           7
1787 0.492394            2       4           7
1788 0.942320            2       4           7
1789 0.803141            2       4           7
1790 0.998646            2       4           7
1791 0.671458            2       4           7
1792 0.627886            2       4           7
1793 0.526776            2       4           7
1794 0.037078            2       4           7
1795 0.138563            2       4           7
1796 0.802136            2       4           7
1797 0.410651            2       4           7
1798 0.962336            2       4           7
1799 0.583450            2       4           7
1800 1.000000            2       4           7
1801 0.824607            2       4           7
1802 0.813784            2       4           7
1803 0.957463            2       4           7
1804 0.019310            2       4           7
1805 0.110518            2       4           7
1806 0.881848            2       4           7
1807 0.753077            2       4           7
1808 0.366385            2       4           7
1809 0.959218            2       4           7
1810 0.425473            2       4           7
1811 0.251150            2       4           7
1812 0.869238            2       4           7
1813 0.825609            2       4           7
1814 0.735201            2       4           7
1815 0.573958            2       4           7
1816 0.625371            2       4           7
1817 0.673210            2       4           7
1818 0.156187            2       4           7
1819 0.204820            2       4           7
1820 0.692343            2       4           7
1821 0.543960            2       4           7
1822 0.766668            2       4           7
1823 0.816986            2       4           7
1824 0.539074            2       4           7
1825 1.000000            2       4           7
1826 0.553093            2       4           7
1827 0.619235            2       4           7
1828 0.655571            2       4           7
1829 0.923005            2       4           7
1830 0.893514            2       4           7
1831 0.914492            2       4           7
1832 0.101867            2       4           7
1833 0.250502            2       4           7
1834 0.393838            2       4           7
1835 0.519905            2       4           7
1836 0.604422            2       4           7
1837 0.912187            2       4           7
1838 0.200122            2       4           7
1839 0.436068            2       4           7
1840 0.639561            2       4           7
1841 0.926052            2       4           7
1842 0.906611            2       4           7
1843 0.925843            2       4           7
1844 0.368472            2       4           7
1845 0.357581            2       4           7
1846 0.199227            2       4           7
1847 0.481031            2       4           7
1848 0.629285            2       4           7
1849 0.930836            2       4           7
1850 0.668172            2       4           7
1851 0.463196            2       4           7
1852 0.664880            2       4           7
1853 0.685129            2       4           7
1854 0.723366            2       4           7
1855 0.705580            2       4           7
1856 0.870920            2       4           7
1857 0.862724            2       4           7
1858 0.153462            2       4           7
1859 0.169016            2       4           7
1860 0.237307            2       4           7
1861 0.088309            2       4           7
1862 0.162494            2       4           7
1863 0.096576            2       4           7
1864 0.546137            2       4           7
1865 0.733085            2       4           7
1866 0.540812            2       4           7
1867 0.438216            2       4           7
1868 0.937492            2       4           7
1869 0.887388            2       4           7
1870 0.587932            2       4           7
1871 0.803156            2       4           7
1872 1.000000            2       4           7
1873 0.695838            2       4           7
1874 0.768375            2       4           7
1875 0.947091            2       4           7
1876 0.010056            2       4           7
1877 0.051858            2       4           7
1878 0.929356            2       4           7
1879 0.852362            2       4           7
1880 0.707494            2       4           7
1881 0.552006            2       4           7
1882 0.339290            2       4           7
1883 0.243338            2       4           7
1884 0.858392            2       4           7
1885 0.832515            2       4           7
1886 0.913360            2       4           7
1887 0.639894            2       4           7
1888 0.625350            2       4           7
1889 0.655558            2       4           7
1890 0.252635            2       4           7
1891 0.258472            2       4           7
1892 0.554150            2       4           7
1893 0.465607            2       4           7
1894 0.918468            2       4           7
1895 0.890527            2       4           7
1896 1.000000            2       4           7
1897 0.922749            2       4           7
1898 0.711331            2       4           7
1899 0.603736            2       4           7
1900 0.735372            2       4           7
1901 0.835771            2       4           7
1902 0.933964            2       4           7
1903 0.941336            2       4           7
1904 0.125235            2       4           7
1905 0.101598            2       4           7
1906 0.412806            2       4           7
1907 0.663492            2       4           7
1908 0.747909            2       4           7
1909 0.657221            2       4           7
1910 0.472343            2       4           7
1911 0.445495            2       4           7
1912 0.604692            2       4           7
1913 0.775820            2       4           7
1914 0.910683            2       4           7
1915 0.873936            2       4           7
1916 0.451377            2       4           7
1917 0.413474            2       4           7
1918 0.380695            2       4           7
1919 0.462973            2       4           7
1920 0.930888            2       4           7
1921 0.673009            2       4           7
1922 0.737226            2       4           7
1923 0.652289            2       4           7
1924 0.966181            2       4           7
1925 0.968151            2       4           7
1926 0.760091            2       4           7
1927 0.729722            2       4           7
1928 0.804491            2       4           7
1929 0.775378            2       4           7
1930 0.167790            2       4           7
1931 0.283804            2       4           7
1932 0.081929            2       4           7
1933 0.088236            2       4           7
1934 0.153669            2       4           7
1935 0.127443            2       4           7
1936 0.683736            2       4           7
1937 0.770200            2       4           7
1938 0.508848            2       4           7
1939 0.396972            2       4           7
1940 0.950438            2       4           7
1941 0.926443            2       4           7
1942 0.586163            2       4           7
1943 0.723154            2       4           7
1944 1.000000            2       4           7
1945 1.000000            2       4           7
1946 0.845633            2       4           7
1947 0.991473            2       4           7
1948 0.763990            2       4           7
1949 0.789064            2       4           7
1950 0.923760            2       4           7
1951 0.946030            2       4           7
1952 0.127775            2       4           7
1953 0.079673            2       4           7
1954 0.167272            2       4           7
1955 0.228486            2       4           7
1956 0.896105            2       4           7
1957 0.823069            2       4           7
1958 0.896842            2       4           7
1959 0.764717            2       4           7
1960 0.575969            2       4           7
1961 0.450479            2       4           7
1962 0.952352            2       4           7
1963 0.947884            2       4           7
1964 0.322666            2       4           7
1965 0.452236            2       4           7
1966 0.278962            2       4           7
1967 0.269577            2       4           7
1968 0.878258            2       4           7
1969 0.868788            2       4           7
1970 0.835090            2       4           7
1971 0.849236            2       4           7
1972 0.731213            2       4           7
1973 0.786665            2       4           7
1974 0.927982            2       4           7
1975 0.686491            2       4           7
1976 0.773190            2       4           7
1977 0.742423            2       4           7
1978 0.779641            2       4           7
1979 0.807457            2       4           7
1980 0.140368            2       4           7
1981 0.151710            2       4           7
1982 0.196288            2       4           7
1983 0.200379            2       4           7
1984 0.629578            2       4           7
1985 0.419446            2       4           7
1986 0.539876            2       4           7
1987 0.541309            2       4           7
1988 0.792496            2       4           7
1989 0.773807            2       4           7
1990 0.713823            2       4           7
1991 0.786846            2       4           7
1992 0.912345            2       4           7
1993 0.566353            2       4           7
1994 1.000000            2       4           7
1995 0.543118            2       4           7
1996 0.670402            2       4           7
1997 0.778632            2       4           7
1998 0.429540            2       4           7
1999 0.543761            2       4           7
2000 0.672513            2       4           7
2001 0.688058            2       4           7
2002 0.900497            2       4           7
2003 0.885993            2       4           7
2004 0.840911            2       4           7
2005 0.870795            2       4           7
2006 0.656491            2       4           7
2007 0.843283            2       4           7
2008 0.076094            2       4           7
2009 0.064769            2       4           7
2010 0.140792            2       4           7
2011 0.244205            2       4           7
2012 0.402075            2       4           7
2013 0.597626            2       4           7
2014 0.548590            2       4           7
2015 0.546345            2       4           7
2016 0.565315            2       4           7
2017 0.582840            2       4           7
2018 0.763359            2       4           7
2019 0.946888            2       4           7
2020 0.215230            2       4           7
2021 0.384129            2       4           7
2022 0.447224            2       4           7
2023 0.434073            2       4           7
2024 0.662277            2       4           7
2025 0.899673            2       4           7
2026 0.534553            2       4           7
2027 0.840393            2       4           7
2028 0.911335            2       4           7
2029 0.926565            2       4           7
2030 0.939982            2       4           7
2031 0.858059            2       4           7
2032 0.461532            2       4           7
2033 0.383953            2       4           7
2034 0.224559            2       4           7
2035 0.276907            2       4           7
2036 0.218645            2       4           7
2037 0.174030            2       4           7
2038 0.433463            2       4           7
2039 0.486558            2       4           7
2040 0.780199            2       4           7
2041 0.818871            2       4           7
2042 0.927341            2       4           7
2043 0.921136            2       4           7
2044 0.692608            2       4           7
2045 0.573887            2       4           7
2046 0.516764            2       4           7
2047 0.210997            2       4           7
2048 0.675076            2       4           7
2049 0.900067            2       4           7
2050 0.836151            2       4           7
2051 0.857718            2       4           7
2052 0.711724            2       4           7
2053 0.740331            2       4           7
2054 0.863043            2       4           7
2055 0.680746            2       4           7
2056 0.813235            2       4           7
2057 0.662489            2       4           7
2058 0.902825            2       4           7
2059 0.901071            2       4           7
2060 0.269560            2       4           7
2061 0.028583            2       4           7
2062 0.138629            2       4           7
2063 0.080128            2       4           7
2064 0.224655            2       4           7
2065 0.232108            2       4           7
2066 0.123861            2       4           7
2067 0.091711            2       4           7
2068 0.178301            2       4           7
2069 0.171034            2       4           7
2070 0.097760            2       4           7
2071 0.094213            2       4           7
2072 0.545993            2       4           7
2073 0.599441            2       4           7
2074 0.503896            2       4           7
2075 0.561661            2       4           7
2076 0.465444            2       4           7
2077 0.484165            2       4           7
2078 0.455823            2       4           7
2079 0.413106            2       4           7
2080 0.912457            2       4           7
2081 0.931455            2       4           7
2082 0.897924            2       4           7
2083 0.906247            2       4           7
2084 0.599270            2       4           7
2085 0.646288            2       4           7
2086 0.586035            2       4           7
2087 0.714137            2       4           7

1.0.1.4 2.4 Correlations

Selection of possible factor influencing obesity level.

Code
#Create the heatmap with correlation values

#Assuming dataset_num is already defined and contains the relevant columns
cor_matrix <- cor(dataset_num %>%
                    select("physical_act", "freq_alcohol", "obesity_lev", "age", "weight","height", "family_hist", "caloric_food", "vegetable_food", "food_btw_meals", "use_tech", "ch2o", "m_trans", "smoke","nb_meal_day", "calorie_check", "gender"), use = "complete.obs")

#Extract the correlations with 'obesity_lev'
cor_with_obesity_lev <- cor_matrix["obesity_lev",]

#Order variables by their correlation with 'obesity_lev'
ordered_vars <- names(sort(cor_with_obesity_lev, decreasing = TRUE))

#Reorder the correlation matrix based on this order
cor_matrix_ordered <- cor_matrix[ordered_vars, ordered_vars]

#Melt the ordered correlation matrix into long format
cor_long <- melt(cor_matrix_ordered)

ggplot(cor_long, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile() +
  geom_text(aes(label = round(value, 2)), color = "black", size = 2.5, vjust = 0.5, hjust = 0.5) + # Center text within tiles
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
  labs(title = "Correlation Heatmap Ordered by Obesity Level", x = "Variables", y
       = "Variables") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), 
        # Rotate x-axis labels for readability
        axis.text.y = element_text(angle = 45, vjust = 1) 
        # Rotate y-axis labels for readability
  )

1.0.2 3. Exploratory Data Analysis (EDA)

1.0.2.1 3.1 Descriptive statistics and distribution analysis

1.0.2.1.1 Age

Descriptive statistic for Age.

Code
summary(dataset$age)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  14.00   19.92   22.85   24.35   26.00   61.00 
Code
sd(dataset$age, na.rm = TRUE)
[1] 6.368801

Age distribution.

Code
ggplot(dataset, aes(x = age)) +
  geom_histogram(bins = 20, fill = "skyblue", color = "black", alpha = 0.7) +
  labs(title = "Age Distribution", x = "Age", y = "Count")+
  theme_minimal()

The average age of participants is 24.3 years, with a standard deviation of 6.35 years, predominantly concentrated in the 20-30 age range This distribution suggests a young population, which may limit the generalizability of results to older age groups, where risk factors for overweight and obesity may differ.

Age by Obesity Level

Distribution of Age by Obesity Level.

Code
ggplot(dataset, aes(x = age, fill = obesity_lev)) +
  geom_histogram(bins = 20, color = "black", alpha = 0.6) +
  facet_wrap(~ obesity_lev) +
  ggtitle("Distribution of Age by Obesity Level") +
  labs(x = "Age", y = "Count") +
  theme_minimal()

The age distribution varies across obesity levels, with younger ages being more prevalent in lower obesity levels (like Insufficient Weight and Normal Weight), while higher obesity levels tend to have older individuals. This suggests a possible trend where age might correlate with obesity level, especially in higher obesity categories.

Age Distribution by Obesity Level (Violin Plot).

Code
ggplot(dataset, aes(x = obesity_lev, y = age, fill = obesity_lev)) +
  geom_violin(trim = FALSE, alpha = 0.6) +
  geom_boxplot(width = 0.1, color = "black", fill = "white") +
  labs(title = "Age Distribution by Obesity Level", x = "Obesity Level", y = "Age") +
  theme_minimal()

The violin plot shows, more clearly, the spread and density of ages across obesity levels. Younger ages dominate in the lower obesity levels, while there is a wider age range with a higher density around 30–40 years in the higher obesity levels. This pattern supports the idea that older age groups are more likely to fall into higher obesity categories.

Age Distribution with SMOOTH Trend Line for Obesity Probability.

Code
ggplot(dataset, aes(x = age, y = as.numeric(obesity_lev))) +
  geom_jitter(alpha = 0.3) +
  geom_smooth(method = "loess", se = FALSE, color = "blue") +
  labs(title = "Trend of Obesity Level with Age", x = "Age", y = "Obesity Level") +
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

The trend line suggests an increase in obesity level with age until around 30–35, followed by a decrease. This implies that middle age might be a peak period for higher obesity levels, and there may be a trend of reducing obesity levels at older ages.

1.0.2.1.2 Height

Descriptive statistic for Height.

Code
summary(dataset$height)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.450   1.630   1.702   1.703   1.769   1.980 
Code
sd(dataset$height, na.rm = TRUE)
[1] 0.09318594

Height distribution.

Code
ggplot(dataset, aes(x = height)) +
  geom_histogram(bins = 20, fill = "purple", color = "black", alpha = 0.7) +
  labs(title = "Height Distribution", x = "Height (m)", y = "Count") +
  theme_minimal()

Height by gender

Density plot for height distribution by gender.

Code
ggplot(dataset, aes(x = height, fill = gender)) +
  geom_density(alpha = 0.5) +
  labs(title = "Density Plot of Height by Gender", x = "Height", y = "Density") +
  scale_fill_manual(values = c("pink", "lightblue"), name = "Gender", labels = c("Female", "Male")) +
  theme_minimal()

The average height is 1.70 m (SD = 0.09 m), with a notable difference between genders, where males tend to have a higher median height.

Height by Obesity Level

Box Plot of Height by Obesity Level.

Code
ggplot(dataset, aes(x = obesity_lev, y = height, fill = obesity_lev)) +
  geom_boxplot(alpha = 0.6) +
  labs(title = "Height Distribution by Obesity Level", x = "Obesity Level", y = "Height") +
  theme_minimal() +
  theme(legend.position = "none", axis.text.x = element_text(angle = 45, hjust = 1))

The boxplot indicates that individuals with lower obesity levels (e.g., Insufficient Weight, Normal Weight) tend to have a more consistent height range, while those in higher obesity levels (like Obesity Type II and III) show more variability in height. This suggests that weight may be more influential than height alone in determining obesity level.

1.0.2.1.3 Weight

Descriptive statistic for Weight.

Code
summary(dataset$weight)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  39.00   66.00   83.10   86.86  108.02  173.00 
Code
sd(dataset$weight, na.rm = TRUE)
[1] 26.19085

Weight by gender

Density plot for weight distribution by gender.

Code
ggplot(dataset, aes(x = weight, fill = gender)) +
  geom_density(alpha = 0.5) +
  labs(title = "Density Plot of Weight by Gender", x = "Weight", y = "Density") +
  scale_fill_manual(values = c("pink", "lightblue"), name = "Gender", labels = c("Female", "Male")) +
  theme_minimal()

The density plot reveals distinct weight distributions between genders. Females generally weigh less, with a peak around 60-70 units, while males peak around 90-100 and 110-120 units, indicating a tendency toward higher weights. The overlapping region around 80-90 units shows weights common to both genders, though males dominate at higher ranges Weight ranges from 39 to 173 units, with an average (mean) weight of 86.6 units. The median weight is 83 units, with a standard deviation of 26.2, indicating moderate spread.

Weight by obesity level

Ridgeline Plot of Weight by Obesity Level.

Code
ggplot(dataset, aes(x = weight, y = obesity_lev, fill = obesity_lev)) +
  geom_density_ridges(scale = 0.9, alpha = 0.6) +
  labs(title = "Ridgeline Plot of Weight by Obesity Level", x = "Weight", y = "Obesity Level") +
  theme_minimal() +
  theme(legend.position = "none")
Picking joint bandwidth of 2.63

This ridgeline plot shows a clear progression in weight distribution across different obesity levels. As the obesity level increases, the weight distribution shifts progressively to higher ranges. “Normal Weight” and “Insufficient Weight” categories are concentrated at lower weights, while higher obesity types (I, II, and III) peak at significantly greater weights, indicating a strong positive association between weight and obesity level The weight distribution has an average of 86.6 kg and a standard deviation of 26.6 kg.

1.0.2.1.4 Height and Weight

Scatter Plot (height vs weight), colored by obesity level.

Code
ggplot(dataset, aes(x = height, y = weight, color = obesity_lev)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE, aes(group = obesity_lev)) +  # Adds a trend line for each obesity level
  ggtitle("Scatter Plot of Weight vs Height by Obesity Level") +
  theme_minimal() +
  labs(x = "Height", y = "Weight", color = "Obesity Level")
`geom_smooth()` using formula = 'y ~ x'

Facet Grid for Height and Weight by Obesity Level.

Code
ggplot(dataset, aes(x = height, y = weight)) +
  geom_point(alpha = 0.7, aes(color = obesity_lev)) +
  facet_wrap(~ obesity_lev) +
  ggtitle("Facet Grid of Weight and Height by Obesity Level") +
  theme_minimal() +
  labs(x = "Height", y = "Weight", color = "Obesity Level") +
  theme(legend.position = "none")

The scatter plot with trend lines for each obesity level reveals a clear positive correlation between weight and height across all obesity levels. As the obesity level increases, the slope generally becomes steeper, indicating a stronger weight gain relative to height. We created the facet grid to show more clearly the trends to show more clearly how The “Obesity_Type_III” (yellow) category has the steepest slope, suggesting a significant weight increase per unit of height, which is consistent with the highest level of obesity.

Correlation between height and weight.

Code
correlation_height_weight <- cor(dataset$height, dataset$weight, use = "complete.obs")
correlation_height_weight
[1] 0.457468

The correlation observed between height and weight (r = 0.463) aligns with existing literature, confirming the expected positive relationship between these variables.

1.0.2.1.5 Function for EDA on categorical variables

Function for EDA on categorical variables, with separate distributions for gender and obesity levels.

Code
eda_categorical <- function(data, variable, gender_var = "gender", obesity_var = "obesity_lev") {
  cat("\n--- EDA for Categorical Variable:", variable, "---\n")
# Frequency and Proportion Tables
cat("\nFrequency Table:\n")
freq_table <- table(data[[variable]])
print(freq_table)


cat("\nProportion Table (Rounded):\n")
prop_table <- round(prop.table(freq_table), 2)
print(prop_table)
 
# Bar Chart with Counts (distribution)
p1 <- ggplot(data, aes(x = .data[[variable]])) +
  geom_bar(fill = "skyblue", color = "black") +
  ggtitle(paste("Distribution of", variable, "- Counts")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(y = "Count")
  print(p1)

# Distribution by Gender
p2 <- ggplot(data, aes(x = .data[[variable]], fill = .data[[gender_var]])) +
   geom_bar(position = "dodge", color = "black") +
   ggtitle(paste("Distribution of", variable, "by Gender")) +
   labs(fill = "Gender") +
   theme_minimal() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
   labs(y = "Count")
   print(p2)
  
# Dodged Bar Chart for Categorical Variable by Obesity Levels
p3 <- ggplot(data, aes(x = .data[[variable]], fill = .data[[obesity_var]])) +
   geom_bar(position = "dodge", color = "black") +
   ggtitle(paste("Dodged Bar Chart of", variable, "by", obesity_var)) +
   labs(y = "Count", fill = obesity_var) +
   theme_minimal() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1))
   print(p3)
  
# Stacked Bar Chart of Food Between Meals by Obesity Level (Proportions within each Obesity Level)
p4 <- ggplot(data, aes(x = .data[[obesity_var]], fill = .data[[variable]])) +
   geom_bar(position = "fill") +
   scale_y_continuous(labels = scales::percent) +
   ggtitle(paste("Stacked Bar Chart of", variable, "by", obesity_var, "- Proportions within each Obesity Level")) +
   labs(x = obesity_var, y = "Proportion", fill = variable) +
   theme_minimal() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1))
   print(p4)
}

#Food between meals (snacking) 
eda_categorical(dataset, "food_btw_meals", gender_var = "gender", obesity_var = "obesity_lev")

--- EDA for Categorical Variable: food_btw_meals ---

Frequency Table:

        No  Sometimes Frequently     Always 
         0       1761        236         53 

Proportion Table (Rounded):

        No  Sometimes Frequently     Always 
      0.00       0.86       0.12       0.03 

Overall distribution - There are very few who selected “Always” or “no.”

Distribution by Gender - it appears that a slightly higher proportion of males selected “Sometimes” compared to females.

Distribution by Obesity Level - As obesity levels increase, there is a noticeable shift towards “Sometimes” and “Frequently” responses, particularly in higher obesity levels (Type I, II, and III).

Individuals with “Insufficient Weight” and “Normal Weight” show a relatively balanced spread between “no,” “Sometimes,” and “Frequently.”

These observations suggest a potential correlation between the frequency of eating between meals and obesity levels. Individuals who report eating between meals “Sometimes” or “Frequently” seem more likely to have higher obesity levels.

In our further work we can explore the interaction with people who engage in regular physical activity, caloric_food often have increased caloric needs and may snack more frequently. Also ch20, people who drink less water may have more cravings or perceive thirst as hunger, leading them to snack more.

1.0.2.1.6 High-calore food consumption
Code
eda_categorical(dataset, "caloric_food", gender_var = "gender", obesity_var = "obesity_lev")

--- EDA for Categorical Variable: caloric_food ---

Frequency Table:

  no  yes 
 243 1844 

Proportion Table (Rounded):

  no  yes 
0.12 0.88 

Overall distribution - The majority of individuals prefer caloric food, as indicated by the high count of “yes” responses.

Distribution by Gender - Both males and females show a similar pattern, with a strong preference for caloric food (“yes”), although the preference is slightly higher in males.

Distribution by Obesity Level - As obesity levels increase, the preference for caloric food (“yes”) also increases, particularly in higher obesity categories (Obesity Types I, II, and III).

Proportion Analysis by Obesity Level - Individuals with higher obesity levels are overwhelmingly more likely to prefer caloric food, with nearly 100% of those in Obesity Types II and III choosing “yes”.

Count participants who answered “yes” for frequent high-calorie food consumption.

Code
num_high_caloric_consumers <- sum(dataset$caloric_food == "yes")

Calculate the total number of participants in the dataset.

Code
total_participants <- nrow(dataset)

Calculate the percentage of high-calorie consumers.

Code
percentage_high_caloric_consumers <- (num_high_caloric_consumers / total_participants) * 100
percentage_high_caloric_consumers
[1] 88.35649

A notable 88.4% of participants report frequent consumption of high-calorie foods, which may contribute to weight gain. This trend highlights the need for dietary interventions focused on reducing high-calorie intake.

1.0.2.1.7 Alcohol consumption

Frequence in consumption of alcohol.

Code
eda_categorical(dataset, "freq_alcohol", gender_var = "gender", obesity_var = "obesity_lev")

--- EDA for Categorical Variable: freq_alcohol ---

Frequency Table:

        No  Sometimes Frequently     Always 
       636       1380         70          1 

Proportion Table (Rounded):

        No  Sometimes Frequently     Always 
      0.30       0.66       0.03       0.00 

The majority of individuals report drinking alcohol “Sometimes” or “no,” with very few indicating “Frequently” or “Always.”.

Distribution by Gender - Both males and females primarily drink “Sometimes” or “no,” with females showing a slightly higher preference for “Sometimes”.

Distribution by Obesity Level - In lower obesity levels (Insufficient and Normal Weight), a significant proportion reports “no” alcohol consumption,In higher obesity levels, particularly Obesity_Type_I and above, there is an increased tendency to drink “Sometimes,” while “no” responses decline.

Proportion Analysis by Obesity Level - Individuals with higher obesity levels are overwhelmingly more likely to prefer caloric food, with nearly 100% of those in Obesity Types II and III choosing “yes”.

Line plot to show the pattern.

Code
# Prepare the data summary for 'Sometimes' and 'No' responses
data_summary <- dataset %>%
  filter(freq_alcohol %in% c("Sometimes", "No")) %>%
  group_by(obesity_lev, freq_alcohol) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(obesity_lev) %>%
  mutate(total = sum(count), proportion = count / total) %>%
  ungroup()

# Check if data_summary has the expected columns
print(head(data_summary))
# A tibble: 6 × 5
  obesity_lev         freq_alcohol count total proportion
  <ord>               <ord>        <int> <int>      <dbl>
1 Insufficient_Weight No             117   266      0.440
2 Insufficient_Weight Sometimes      149   266      0.560
3 Normal_Weight       No             104   263      0.395
4 Normal_Weight       Sometimes      159   263      0.605
5 Overweight_Level_I  No              50   260      0.192
6 Overweight_Level_I  Sometimes      210   260      0.808
Code
ggplot(data_summary, aes(x = obesity_lev, y = proportion, group = freq_alcohol, color = freq_alcohol)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 3) +
  scale_y_continuous(labels = scales::percent) +
  ggtitle("Proportion of 'Sometimes' and 'No' Responses for Alcohol Frequency by Obesity Level") +
  labs(x = "Obesity Level", y = "Proportion", color = "Alcohol Frequency") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) 

The proportion of individuals who drink alcohol “Sometimes” increases with higher obesity levels, peaking in Obesity_Type_III. In contrast, the likelihood of abstaining from alcohol (“no”) decreases as obesity levels rise. This pattern suggests that moderate alcohol consumption may be associated with higher obesity levels, while abstention is more common among those with lower obesity levels.

A possible interaction to investigate later is between alcohol frequency and caloric food preference, as both behaviors seem linked to higher obesity levels. Exploring this could reveal if individuals with a preference for caloric foods and moderate alcohol consumption have a compounding effect on obesity risk. This investigation could help clarify whether combined lifestyle factors contribute more significantly to higher obesity levels than each factor alone.

Monitoring of the calories in the day.

Code
eda_categorical(dataset, "calorie_check", gender_var = "gender", obesity_var = "obesity_lev")

--- EDA for Categorical Variable: calorie_check ---

Frequency Table:

  no  yes 
1991   96 

Proportion Table (Rounded):

  no  yes 
0.95 0.05 

The vast majority of individuals do not check calories.

Distribution by Gender - Only a small percentage in each gender reporting that they check calories.

Distribution by Obesity Level - There is a slight increase in calorie-checking behavior among individuals with lower obesity levels (Insufficient and Normal Weight).

Proportion Analysis by Obesity Level - Calorie-checking behavior slightly decreases as obesity levels increase.

Code
data_summary <- dataset %>%
  group_by(obesity_lev, calorie_check) %>%
  summarise(count = n(), .groups = "drop") %>%
  mutate(total = sum(count), proportion = count / total)

ggplot(data_summary, aes(x = obesity_lev, y = proportion, group = calorie_check, color = calorie_check)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  scale_y_continuous(labels = scales::percent) +
  scale_color_manual(values = c("no" = "lightcoral", "yes" = "lightblue")) +
  labs(title = "Proportion of Calorie Checking by Obesity Level", x = "Obesity Level", y = "Proportion", color = "Calorie Check") +
  theme_minimal()
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

1.0.2.1.8 Vegetable consumption

Distribution of vegetable consumption frequency (vegetable_food): initially, a bar chart was created. This allowed us to clearly identify the presence of non-integer data. Values are rounded to the nearest integer, and the categories are renamed to make the informational content immediately understandable.

Code
dataset$vegetable_food <- round(dataset$vegetable_food)
ggplot(dataset, aes(x = factor(vegetable_food, levels = c(1, 2, 3), 
                               labels = c("Rarely", "Sometimes", "Often")))) +
  geom_bar(fill = "skyblue", color = "black") +
  labs(title = "Vegetable Consumption Frequency", x = "Frequency of Vegetable Consumption", y = "Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=0.5))

Vegetable intake is generally moderate, with a mean score of 2.4 on a 1-to-3 scale (1 = “Rarely”, 3 = “Often”).

However, a detailed distribution of consumption and an analysis of associations with other risk factors are necessary to make informed statements about the quality of dietary habits and potential health risks.

1.0.2.1.9 Number of meals per day

The same issues as with the previous variable were initially encountered in the graphical representation. Similarly, the values were rounded and the labels were renamed.

Code
ggplot(dataset, aes(x = factor(round(nb_meal_day), levels = c(1, 2, 3, 4, 5), 
                               labels = c("1 Meal", "2 Meals", "3 Meals", "4 Meals", "5+ Meals")))) + 
  geom_bar(fill = "orange", color = "black") + 
  labs(title = "Number of Meals per Day", x = "Meals per Day", y = "Count") + 
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 0.5))

Most participants consume three meals daily, with a mean of 2.7 and a standard deviation of 0.5.

Calculate the correlation between the number of meals per day and weight. Select the variables for correlation: number of meals per day and weight.

Code
correlation_meals_weight <- cor(dataset$nb_meal_day, dataset$weight, use = "complete.obs")
correlation_meals_weight
[1] 0.09214947

The correlation between the number of meals per day and weight is relatively weak; this suggests that meal frequency alone may not have a direct impact on weight; instead, other factors like meal quality and portion sizes could play a more significant role.

1.0.2.1.10 Physical activity

Plot histogram and density.

Code
ggplot(dataset, aes(x = physical_act)) +
  geom_histogram(aes(y = ..density..), bins = 30, fill = "skyblue", color = "black", alpha = 0.6) +
  geom_density(color = "darkblue", size = 1) +
  ggtitle("Histogram and Density of Physical Activity") +
  theme_minimal() +
  labs(x = "Physical Activity", y = "Density")
Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.

The histogram and density plot reveal that physical activity levels have distinct peaks at 0, 1, 2, and 3, suggesting that these values are common reported levels. Intermediate values, likely due to synthetic data or SMOTE, are also present but less frequent.

Violin plot by category.

Code
ggplot(dataset, aes(x = obesity_lev, y = physical_act, fill = obesity_lev)) +  # Replace 'obesity_lev' with any category variable
  geom_violin(trim = FALSE) +
  geom_boxplot(width = 0.1, color = "black", fill = "white") +
  ggtitle("Violin Plot of Physical Activity by Obesity Level") +
  theme_minimal() +
  labs(x = "Obesity Level", y = "Physical Activity") +
  theme(legend.position = "none")

Individuals with lower obesity levels (e.g., Insufficient and Normal Weight) have a wider spread of physical activity levels, often skewed toward higher activity #Higher obesity levels show a tendency toward lower physical activity.

Scatter Plot with Age.

Code
ggplot(dataset, aes(x = age, y = physical_act)) +  # Replace 'age' with another continuous variable
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", color = "blue", se = FALSE) +
  ggtitle("Scatter Plot of Physical Activity vs Age") +
  theme_minimal() +
  labs(x = "Age", y = "Physical Activity")
`geom_smooth()` using formula = 'y ~ x'

There appears to be a negative trend between age and physical activity levels, with younger individuals tending to report higher physical activity levels.

1.0.2.1.11 Water consumption

Plot histogram and density for water consumption.

Code
ggplot(dataset, aes(x = ch2o)) +
  geom_histogram(aes(y = ..density..), bins = 30, fill = "skyblue", color = "black", alpha = 0.6) +
  geom_density(color = "darkblue", size = 1) +
  ggtitle("Histogram and Density of Comsumption of Water") +
  theme_minimal() +
  labs(x = "CH2O", y = "Density")

This histogram and density plot of daily water consumption (CH2O) shows a clear peak at 2 liters, indicating that most individuals consume around this amount. This aligns with scientific literature, which generally recommends an average daily water intake of about 2 liters for optimal health.

Facet Plot (CH2O by Obesity Level and Gender).

Code
ggplot(dataset, aes(x = ch2o, fill = gender)) +
  geom_histogram(bins = 20, alpha = 0.6, position = "dodge") +
  facet_wrap(~ obesity_lev) +
  ggtitle("Histogram of CH2O by Obesity Level and Gender") +
  theme_minimal() +
  labs(x = "CH2O", y = "Count", fill = "Gender")

An uncommon trend is shown in Obesity Type II predominantly includes males, while Obesity Type III mostly includes females, highlighting a gender disparity at higher obesity levels in water consumption patterns.

Scatter Plot (ch2o vs Age).

Code
ggplot(dataset, aes(x = age, y = ch2o)) +
  geom_point(alpha = 0.5, color = "darkblue") +
  geom_smooth(method = "lm", color = "red", linetype = "dashed") +
  ggtitle("Scatter Plot of CH2O vs Age with Trendline") +
  theme_minimal() +
  labs(x = "Age", y = "CH2O")
`geom_smooth()` using formula = 'y ~ x'

Slight downward trend suggests a minor decrease in water consumption with age, though variability remains high across all ages.

Violin Plot by Gender.

Code
ggplot(dataset, aes(x = gender, y = ch2o, fill = gender)) +
  geom_violin(trim = FALSE, alpha = 0.7) +
  ggtitle("Violin Plot of CH2O by Gender") +
  theme_minimal() +
  labs(x = "Gender", y = "CH2O") +
  theme(legend.position = "none")

Males show more variability in water consumption compared to females, with similar median values around 2. The water consumption variable (CH2O) shows a predominant consumption level around 2 across different obesity levels, with minor variations by gender and obesity category. Originally, CH2O values were discrete (1.0, 2.0, 3.0), suggesting categorical consumption levels. However, after applying the SMOTE algorithm to address class imbalance, interpolated values emerged, resulting in a more continuous distribution. This variable’s distribution across obesity levels, along with gender differences at extreme obesity levels (e.g., more males in Obesity Type II and females in Obesity Type III), indicates that CH2O could be a valuable predictor for obesity, capturing both consumption habits and subtle demographic patterns.

1.0.2.1.12 Technology utilization

Histogram with Density.

Code
ggplot(dataset, aes(x = use_tech)) +
  geom_histogram(aes(y = ..density..), bins = 30, fill = "lightblue", color = "black", alpha = 0.6) +
  geom_density(color = "blue", size = 1) +
  labs(title = "Histogram and Density of Use of Technology", x = "Use of Technology", y = "Density") +
  theme_minimal()

The histogram shows a strong concentration at discrete values (0, 1, and 2), likely reflecting the original categorical nature of the data before SMOTE. This also aligns with observed density peaks.

Density of Use of Technology by Obesity Level.

Code
ggplot(dataset, aes(x = use_tech, fill = obesity_lev)) +
  geom_density(alpha = 0.5) +
  labs(title = "Density of Use of Technology by Obesity Level", x = "Use of Technology", y = "Density") +
  theme_minimal()

The density plot reveals distinct peaks in technology usage across obesity levels, with some levels like Obesity_Type_III having higher peaks, suggesting varied levels of technology use in these categories.

Boxplot by Obesity Level.

Code
ggplot(dataset, aes(x = obesity_lev, y = use_tech, fill = obesity_lev)) +
  geom_boxplot() +
  labs(title = "Boxplot of Use of Technology by Obesity Level", x = "Obesity Level", y = "Use of Technology") +
  theme_minimal() +
  theme(legend.position = "none")

Technology use shows some variation across obesity levels, with certain categories (like Obesity_Type_I and Obesity_Type_III) showing higher median usage compared to others.

Scatter Plot with Age.

Code
ggplot(dataset, aes(x = age, y = use_tech)) +
  geom_point(alpha = 0.4, color = "blue") +
  geom_smooth(method = "lm", color = "red", linetype = "dashed") +
  labs(title = "Scatter Plot of Use of Technology vs Age", x = "Age", y = "Use of Technology") +
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

There is a noticeable negative correlation between age and technology use, indicating younger individuals tend to use technology more than older ones.

The ‘Use of Technology’ variable shows a clear trend where younger individuals use technology more frequently, as seen in the negative correlation with age. Differences in technology usage across obesity levels suggest it might have predictive value in distinguishing between levels, although the SMOTE algorithm has introduced interpolated values that blur strict categories. This variable could therefore help predict obesity levels, especially if technology usage patterns are indicative of lifestyle factors associated with obesity.

1.0.3 4. Analysis overview of statistical methods and model selection

In the present analytical endeavor, we plan to employe a regression model approach to elucidate the intricate dynamics between a set of independent variables, which serve as the predictors, and Obesity Level as a singular dependent variable which is the outcome.

The rationale behind the selection of regression modeling stems from its established robustness as a statistical methodology, particularly adept at unraveling and quantifying the interrelations among variables. This is paramount, considering our overarching objective to forecast outcomes and to meticulously evaluate the repercussions that alterations in the predictor variables may have on the target variable.

Based on our exploratory data analysis, indications of potential outliers emerged within our dataset. However, upon closer examination, these values represent extreme data points that remain plausible given the context of our study. Consequently, our approach involves building two regression models: one that includes these extreme values and one that excludes them. The objective is to examine the impact of these extreme data points on the predictive performance of the model, analyzing how their presence or absence influences the resulting predictions and model behavior.

The idea behind the adoption of regression analysis is twofold. Firstly, it affords a nuanced understanding of the extent to which each predictor influences the outcome. Secondly, it provides a suite of statistical metrics that facilitate the evaluation of the model’s capacity to elucidate the variance in the data. Through regression analysis, we can ascertain the presence of statistically significant linkages between the variables under scrutiny and quantify the magnitude and trajectory of these associations. This method endows us with coefficients that reflect the anticipated alteration in the dependent variable corresponding to a unit change in the predictors, whilst controlling for the constancy of other variables. That answers the first part of our research question. In top of that the regression model will permit us to ascertain the extent to which our independent variables account for the variability observed in the dependent variable (Assess Predictive Power), but we also will be able to delineate the individual impact magnitudes exerted by each predictor variable and to validate the statistical significance of these effects(Quantify Effects:).

At last, by integrating pertinent covariates and control variables into the model, we aim to attenuate biases and segregate the influence of the primary predictors on the outcome, thereby enhancing the accuracy of our findings.By looking at R², the P-values and the standardized coefficients we should be able to understand what are the key factors that can influence the weight condition of a person( obesity level).

To ensure the performance of the model we will need to check the linearity between the values, the normality of residuals and the homogeneity of Variance. And lastly we will check which non significant variable Variance Inflation Factor (VIF) is a statistical measure used to detect multicollinearity in a regression model. Multicollinearity occurs when two or more independent variables in a model are highly correlated, meaning they contain redundant information. High multicollinearity can distort the estimates of coefficients, making it difficult to interpret the individual effect of each predictor.

We also want to build a predictive model. The EDA and the regression model will likely show that some of the key factors of our dataset are useful to make prediction about the type of weight someone will have. Once we identified relationships within our data, we aim to make reliable predictions about future outcomes. the regression will also help us understand which variables have the most significant impact on obesity level. And by using other statistical metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R², we will assess how well the model performs and refine it as needed to improve accuracy.

1.0.4 5. Conclusion

So far, we have conducted a comprehensive exploration and preparation of our dataset, focusing on understanding the influence of lifestyle factors on obesity within a sample from Mexico, Peru, and Colombia. The dataset, which was pre-processed with SMOTE to address class imbalance, has provided us with balanced obesity categories, facilitating an in-depth analysis of key variables such as eating habits, physical activity, and alcohol consumption. Through correlation analysis, we identified the variables with the strongest associations to obesity levels, helping to guide our selection of factors for inclusion in the next modeling phase. Additionally, we have thoroughly cleaned and structured the data, renaming variables for clarity, formatting categorical variables, and removing duplicates to ensure a solid foundation for robust modeling.

The next steps involve constructing regression models to analyze the relationships and predictive power of these selected factors on obesity levels. Specifically, we will develop two versions of the model—one that includes extreme values and one that excludes them—to evaluate the impact of outliers on model accuracy and stability. Key metrics such as R², P-values, and VIF will be used to confirm the reliability of the model and address potential multicollinearity issues. Following this, we will build and fine-tune a predictive model using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² to validate and enhance performance.

These efforts will culminate in a final report that, while primarily an exercise and not applicable in real-world contexts, highlights our findings and offers insights into the most influential lifestyle factors affecting obesity. This analysis aims to provide actionable recommendations within a simulated scenario, illustrating how data-driven insights could support public health strategies focused on obesity reduction.

1.1 Next Steps

Outline the next steps planned for completing the project, such as refining analyses, adding new methods, or addressing outstanding data issues.

1.2 Final Thoughts

Briefly reflect on any challenges or limitations encountered so far and how these might be addressed in the final report.